JP2013512447A - Biochemical analyzer - Google Patents

Biochemical analyzer Download PDF

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JP2013512447A
JP2013512447A JP2012541570A JP2012541570A JP2013512447A JP 2013512447 A JP2013512447 A JP 2013512447A JP 2012541570 A JP2012541570 A JP 2012541570A JP 2012541570 A JP2012541570 A JP 2012541570A JP 2013512447 A JP2013512447 A JP 2013512447A
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module
modules
performance
biochemical analysis
configured
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JP5873023B2 (en
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ブラウン、クライブ、ギャビン
ウィルコックス、ジェイムズ、ピーター
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オックスフォード ナノポール テクノロジーズ リミテッド
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Priority to GB201016614A priority patent/GB201016614D0/en
Priority to PCT/GB2010/002206 priority patent/WO2011067559A1/en
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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N27/00Investigating or analysing materials by the use of electric, electro-chemical, or magnetic means
    • G01N27/26Investigating or analysing materials by the use of electric, electro-chemical, or magnetic means by investigating electrochemical variables; by using electrolysis or electrophoresis
    • G01N27/416Systems
    • G01N27/447Systems using electrophoresis
    • G01N27/44756Apparatus specially adapted therefor
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/483Physical analysis of biological material
    • G01N33/487Physical analysis of biological material of liquid biological material
    • G01N33/48707Physical analysis of biological material of liquid biological material by electrical means
    • G01N33/48721Investigating individual macromolecules, e.g. by translocation through nanopores
    • CCHEMISTRY; METALLURGY
    • C12BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12QMEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6869Methods for sequencing
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B30/00ICT specially adapted for sequence analysis involving nucleotides or amino acids
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
    • G01N15/10Investigating individual particles
    • G01N15/14Electro-optical investigation, e.g. flow cytometers
    • G01N15/1456Electro-optical investigation, e.g. flow cytometers without spatial resolution of the texture or inner structure of the particle, e.g. processing of pulse signals
    • G01N15/1459Electro-optical investigation, e.g. flow cytometers without spatial resolution of the texture or inner structure of the particle, e.g. processing of pulse signals the analysis being performed on a sample stream
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
    • G01N15/10Investigating individual particles
    • G01N15/14Electro-optical investigation, e.g. flow cytometers
    • G01N15/1456Electro-optical investigation, e.g. flow cytometers without spatial resolution of the texture or inner structure of the particle, e.g. processing of pulse signals
    • G01N15/1463Electro-optical investigation, e.g. flow cytometers without spatial resolution of the texture or inner structure of the particle, e.g. processing of pulse signals using image analysis for extracting features of the particle

Abstract

  The analytical instrument comprises a plurality of modules connected to each other via a data network, each module comprising an analytical device operable to perform a biochemical analysis of the sample. Each module includes a control unit that controls the operation of the analyzer. The controller can be addressed to select any number of modules that operate as a cluster for performing a common biochemical analysis. Based on performance measurements derived from the output data generated by the modules, the controller uses a common biochemistry to determine the behavior of each module's analyzer in order to meet global performance targets. Communicate over the data network repeatedly during the analysis. By configuring the instrument as a module that interacts in this way, an analytical instrument that can be scaled is provided.

Description

  The first and second aspects of the present invention provide a sample, such as biochemical analysis using nanopores, which produces output data of multiple parallel channels representing the results of sequencing and / or biochemical analysis of polynucleotides. The present invention relates to a device for performing biochemical analysis of food. A third aspect of the present invention relates to operations in biochemical analysis of samples using nanopores, such as polynucleotide sequencing.

  With respect to the first and second aspects of the present invention, there are many types of biochemical analyzes that produce output data for multiple parallel channels. Devices that perform such biochemical analysis in an automated fashion are known, and these devices provide efficiency in obtaining large amounts of output data that is unique to biochemical analysis.

  By way of example only, one such type of biochemical analysis that produces output data for multiple parallel channels is DNA sequencing. In general, conventional DNA sequencing equipment and laboratory equipment are based on models in which the equipment operates as a stand-alone device. Normally, the device executes one measurement task within a limited time based on a predetermined end condition. We sometimes describe this design model as “monolithic”.

  As an example, DNA sequencing is an inherently high throughput experimental technique. Experiments span a wide variety of data sizes and times, and the data produced is very complex and non-uniform and requires thorough processing downstream. The nature of research on DNA sequencing makes it difficult to treat the analytical core of the instrument system as a black box measurement device. There is a growing need for scalable systems for DNA sequencing that can both scale up and down. This has been driven by recent market demands for sequencing more and more everything, all cheaply, quickly and effectively. Therefore, the sequencing system needs to be able to cope with different workflows and to pipeline various types and sizes of samples according to use cases. This is preferably done efficiently and economically. The measurement artifacts associated with the substrate, or how the substrate was prepared should not negate efficient processing in the instrument and lead to extra downtime or unnecessary reagents. Research laboratories that can operate efficient factory-based sequencing processes will dominate low-cost, high-throughput applications. However, these requirements are difficult to realize.

  Current monolithic DNA sequencing instruments are difficult to scale to analyze at various scales. These instruments cannot be designed to fit very large factory-based operations, but at the same time can be accessible to unskilled laboratory staff in small projects. In general, scalability for current DNA sequencing instruments is provided by increasing the amount of data that the instrument can generate during the execution of a single analysis performed by a single instrument. However, modularity and flexibility are limited, and in order to achieve scalability, users can disassemble the boards, add labels to make them individually addressable, and the sequencer response There is a need to appeal to the means to disassemble the chamber. In either case, artifacts are introduced and there is an inherent limit as to how much scale can be achieved for modularity without a complete redesign of the equipment itself. That is, the basic design of monolithic devices has built-in resource limitations that hinder the ability to handle the demands of real-world workflows.

  In many DNA sequencing instruments, individual strands, or clonally amplified colonies of limited length of DNA, are collected on a face or bead. Typically, this array of faces / beads is placed in a flow cell that allows reagents to pass across them, thereby adding various types of chemical reactions that allow for the decoding of DNA. The biochemical analysis process in many instruments uses stepwise and periodic chemical reactions, and then incorporates, anneals, chemically labeled fluorescent probes that allow the decoding of DNA during the experiment. Or an imaging step is used to detect removal.

  During the base identification phase, in many systems, a high resolution imaging device takes a picture of the entire flow cell surface as an image of a continuous tiled array. In some techniques, a single region is imaged very quickly and the chemical reaction cycle is detected in real time as the base is incorporated asynchronously.

  In general, for continuous imaging in a synchronous chemical reaction-based system, the entire imaging step takes a significant amount of time and generally before the user can acquire and analyze the data. It is necessary to determine whether a preset number of chemical reaction cycles, or preset run times, have been completed, and that the experiment has been successful and has provided sufficiently useful information. In general, only following the analysis, the user can determine whether the experiment was successful, and if successful, a completely new analytical run needs to be performed, Repeat until enough data is collected. In many cases, each run has a fixed cost derived from the price of the reagent. Thus, the price of success is difficult to determine in advance, as is the time to results.

  For many instruments, a single run takes at least a few days, or often weeks, and there is a significant potential for instrument failure during the experiment, generally leading to data truncation or even loss of all data. Higher output per run can be achieved by packing more DNA molecules into the flow cell, but this tends to increase imaging time depending on device resolution and speed / sensitivity, and for net throughput Improvement will be limited. For example, a company called Helicos BioScience sells a device called Helicope with 600-800M DNA fragments attached to two flow cells, and a company called Illumina calls a device called Genome Analyzer for 80M-100M DNA fragments. Is selling. In comparison, Genome Analyzer takes about 1 to 2 hours per base, whereas Helicoscope takes about 6 hours to capture and image a new base for each chain. Thus, the two devices are each best suited for different scale tasks.

  These vendors of such instruments understand that users do not necessarily require large-scale data output on a sample, but it is modular, flexible, and useful due to large-scale data output. In order to allow the user to measure more than one sample per flow cell, even if the data output per sample is concomitantly reduced, generally individually The surface area is physically divided into addressable parts (eg 8 subchannels on the flow cell in the case of Genome Analyzer, ie “lanes”, 25 subchannels per flow cell in the case of Helicoscope). Yes. One such region would still produce a DNA sequence of at least 250 Mb, thereby causing large oversampling for samples containing small genomes, eg, a typical bacteria at 0.5 Mb Will be covered at least 500 times. This example illustrates the inefficient use of equipment and reagents both in terms of time and cost to the user.

  For users, one of the additional problems experienced with existing metrology facilities is that throughput can be measured across the entire flow cell surface, regardless of how little fragment / strand sequencing of DNA / sample is required. This is connected with the cycle time. Current equipment has only one processing section (camera / flow cell surface) and the task of measuring each sample cannot be sufficiently divided to give the desired output to the user.

  A further problem for users is that not only does the user have to know in advance whether or not success in driving is guaranteed, but it is not only necessary to pay the cost of reagents across the surface to achieve the user's results, but also the initial investment in equipment. It is necessary to pay the cost for the processing section for depreciation.

  An example of a more complicated problem is that during the biochemical analysis process, bases are not uniformly added to each available fragment (eg, some fragments are A to C). Have a uniform amount by chance and will consist of repeated homopolymers), not necessarily measured with uniform accuracy (cluster dephasing, unfocused areas in the flow cell ( out-of-focus area), enzyme / polymerase disruption, background signal increase). This means that some areas in the flow cell will generate more data than others, but due to the nature of a single processor, a single processor can provide useful and high quality information. It cannot be adapted to maximize the area it produces or to focus on areas that do not provide enough data.

  In summary, existing systems operate for a defined period of time, and thus cost, but provide information for a fixed number of bases to the user with variable measurement quality. When conducting various DNA sequencing experiments given a range of interest that is of interest to the user, the end result for that user is extremely inefficient in time and cost. This is particularly inefficient when the user wants to analyze multiple samples in parallel in a project based on a given class of sequencing device.

  Although a DNA sequencing instrument has been described as an illustrative example, problems of similar nature arise when designing instruments for a wide range of biochemical analyzes that produce large amounts of output data in multiple parallel channels. sell.

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  The first and second aspects of the present invention aim to alleviate some of these challenges when scaling instruments for performing biochemical analyses.

  With regard to the third aspect of the present invention, in recent years there has been a marked development in biochemical analysis of samples using nanopores. Nanopores are small pores in an electrically insulating layer, and can be formed by protein pores or channels introduced into an amphiphilic membrane. Nanopores allow for the flow of ions through the amphipathic membrane and modulated by the nanopore based on interaction with the analyte, thereby enabling the nanopore to perform biochemical analysis. Various types of nanopores and analyzers using them have been developed for various types of biochemical analyses. One example that is a commercial object is the use of nanopores for sequencing polynucleotides such as DNA. An example of an analyzer for performing biochemical analysis of a sample using nanopores is disclosed in WO-2009 / 0777734.

  Thus, nanopores offer the potential in a platform for commercial scale biochemical analysis. However, in such cases, it would be desirable to achieve efficient handling of the sample in the apparatus in order to maximize throughput and minimize the cost of performing biochemical analysis.

According to a first aspect of the present invention, there is provided an analytical instrument for performing a biochemical analysis, the analytical instrument comprising a plurality of modules,
Each module comprises an analyzer operable to perform a biochemical analysis of the sample, the module configured to generate output data in at least one channel representing the result of the biochemical analysis. The operation of the module can be controlled to change the performance of the module,
The analytical instrument is configured to receive an input that selects any number of modules as a cluster to perform a common biochemical analysis, and is configured to receive an input that represents a global performance goal for the common biochemical analysis. A control system that is configured to control the operation of the modules of the cluster to perform a common biochemical analysis,
The control system is configured to determine performance measurements in each module from the output data generated by the module at least once during the execution of a common biochemical analysis, the control system comprising: (a) all modules Change the control in the operation of the modules of the cluster based on the performance measurements determined for and global performance objectives and / or (b) achieved based on the performance measurements determined for all modules It is configured to take remedies in response to global performance targets that are not possible.

  In the case of DNA sequencing, on behalf of a user with a single device similar to an existing monolithic device, the user has a group of modules in parallel at will, and any number of such modules Can be grouped into large-scale instruments capable of performing common biochemical analyses. Therefore, this instrument is physically paralleled in the sense that this instrument comprises a plurality of modules, each module comprising an analyzer capable of performing a biochemical analysis of the sample. These modules may be the same, but need not be the same. Thus, a common biochemical analysis can be performed across any number of such modules. This generally provides scalability in that a suitable number of modules can be selected to perform biochemical analysis that may require varying amounts of resources depending on its nature. The size and usefulness of the cluster depends on the number of individual modules selected. Depending on the design of the modules and the encapsulated functions, they can be scaled linearly as a single operating unit with reference to an external control system or gateway computer. This scalability results in increased efficiency because an appropriate number of modules can be selected for the current task, thereby freeing other modules for other tasks.

  Any number of such physical modules can be operated, addressed, and treated as a single logical device. However, the size and usefulness of this logical device depends on any number of individual modules that the user has built into an ensemble (ie, a “cluster”).

  Equally important, the individual modules can be addressed by the user (or software) and can operate as stand-alone units that are separate but perform the same core tasks as the ensemble. No further changes in the modules are required to operate those modules individually or in large groups.

  Furthermore, an efficiency improvement exceeding the efficiency improvement due to the scalability of the number of modules is achieved, because the operations in the individual modules can also be intelligently parallelized. This takes advantage of the ability for independent control in the analyzer of each module as follows. From the output data generated by the modules, performance measurements for each module are determined. These performance measurements serve as a basis for controlling the operation of the module to meet global performance goals set by user input or input such as data stored for the biochemical analysis being performed. Used. Such performance targets and performance measurements may be the time to generate output data, the amount of output data, and / or the quality of the output data. This determination is made at least once, preferably repeatedly, or even more continuously during the execution of a common biochemical analysis.

  Control in the operation of the analyzers of individual modules can be changed based on performance measurements for the cluster of modules to meet global performance goals. Typically, the performance of each module can vary based on various factors, so with this control in the operation of each module, the overall performance in the device can be managed to meet global performance goals. This leads to improved efficiency because better utilization is made for the individual modules in the cluster.

  Additionally or alternatively, remedies can be taken depending on global performance goals that are not achievable. For example, various remedies are possible, such as increasing the number of modules that perform a common biochemical analysis, generating an output to notify the user, or even terminating the biochemical analysis. This leads to improved efficiency because better utilization is made for the individual modules in the cluster. For example, it is possible to meet the goal by using additional modules, otherwise the goal will be lost, or the analysis will be terminated and the module will be released for other biochemical analyses. .

  For example, the instrument can measure the amount and quality of output data in real time, and dynamic flexibility to adapt to global performance goals set by the user to maximize time and cost efficiency Can be provided. Further, such an instrument can change the performance of biochemical analysis in any module as required. Examples of such parameters that can be controlled include the temperature of the analyzer, such as parameters in electrical, optical biochemical analysis, fluid parameters, or sampling characteristics in the output data. Examples of electrical parameters are bias voltage and bias current. Examples of fluid parameters are flow rate, sample addition, sample removal, buffer change, reagent addition or removal, nanopore addition or removal, bilayer replacement, and system refresh. Examples of sampling characteristics are amplifier settings such as sample rate, amplifier reset time, and bandwidth, gain, integrator capacitance, etc. Changes in these and other parameters allow performance changes, such as changes in the amount, quality, and speed of output data. For example, the analysis can be terminated when sufficient data is collected, or resources that have already generated sufficient data according to the user's experimental requirements are released from the sample to generate more sufficient data. It is possible to concentrate on the sample in the experiment that is needed.

  For example, if the biochemical analysis is sequencing of polynucleotides in a sample, the instrument can detect, for example, pathogen detection in a broad background, plasma DNA, until a defined number of bases are sequenced. Analyzes at optimal performance until specific sequences are detected, such as detection of cancer mutations in, or for very long periods of time allowing the measurement of very rare polynucleotides, or without user guidance Can operate in many different ways, such as providing a pipeline of

  Such an intelligent and modular sequencing device allows a fundamental review of the workflow to achieve efficient pipelining of experimental equipment and samples. The workflow can be optimized for priority, time, cost, and overall outcome. This provides a significant efficiency improvement over conventional monolithic equipment.

  Furthermore, according to the first aspect of the present invention, it is possible to provide a single remote module that can be connected to other modules to form such a biochemical analyzer, Alternatively, a corresponding method can be provided for the operation of the analyzer.

  Advantageously, these modules can be connected to a data network, for example to be connected to each other over the network on a peer-to-peer basis. This allows the control system to successfully use a data network that facilitates communication and control.

  Although the control system can be implemented in a separate device connected to the network, advantageously, the control system has a control unit operable to control the operation of the module. Prepare for the inside. In this case, the control unit provides the input for selecting any number of modules operating as a cluster to perform a common biochemical analysis, and the user representing the global performance goal for the common biochemical analysis. It may be addressable via a data network to provide input. For example, this can be realized by a control unit configured to provide a user interface to a computer connected to the data network via the data network, for example using a web browser. Next, the control part in the module of a cluster controls operation | movement of each module in order to perform a common biochemical analysis.

  This division of the control system into the control units in the module allows the module itself to be addressed and operated as a single device, simply based on the connection of the module to the network. A large group of modules can be managed to allow an interface for biochemical analysis in an arbitrarily large number of modules, but this is simply done via a network interface to a single cluster. This is because commands can be issued simultaneously. Similarly, feedback and data from any cluster of modules may be collated, logically formatted and addressed, as well as output from a single module. This operational efficiency can be expressed as pipelining and may have a positive impact on upstream sample preparation and downstream output data analysis. Thus, the entire laboratory workflow, from substrate to analysis, can be done more efficiently, no matter how complex or non-uniform the substrate or analysis needs to be. Also, providing a controller for a module means that each module is addressed and operates as a stand-alone unit, and has the ability to perform the same core tasks as the cluster, but at a distance. Thus, no further changes in the modules are required for each module to operate individually or in a large group.

  The corresponding controller in the module of the cluster derives performance measurements for each module from the output data generated by each module, and the performance measurements via the data network to create a basis for further control decisions. Can be configured to communicate. By deriving the performance measurements locally in the module, it is only necessary to share the performance measurements for control. This facilitates control and reduces bottlenecks in the data flow because performance measurements require a much smaller amount of data than output data.

  Controllers in the modules of the cluster may be configured to communicate over the data network to make decisions regarding controlling further operations. This has the advantage that the control system is implemented by providing a control in each of the modules. Thus, a group of modules can operate by simply connecting the modules to the data network without requiring the provision of any additional control system.

  Advantageously, the control system is configured to determine a local performance target for each module based on the global performance target, and the controller in each module operates the module based on the local performance target of that module. Is configured to control. In this way, the control system can change the local performance goal based on the determined performance measurement and the global performance goal in order to change the control in the operation of the modules of the cluster.

  There are various ways to distribute the determination of local performance goals.

  In the first embodiment, this determination can be made in all controllers, for example, each controller determines its module's local performance goal. This allows sharing of the processing load performed by the controller that derives the performance measurements and determines the required action. This also allows scalability in operation and management by avoiding a single gateway system or a bottlenecked computer system.

  In the second embodiment, this determination can be made by one (or a subset) of the controllers. This concentrates the determination of local performance goals on a single controller (or a subset of the controllers in the cluster), which increases the processing burden on that controller, but does not require the processing required to make the decision. It can be easy.

  In the third embodiment, this determination can be made by another integrated control unit similarly connected to the data network. This concentrates the determination of local performance targets on another integrated control unit, thereby reducing the processing burden on the module control unit. This has the burden of requiring an additional integrated controller, but may be advantageous in that it simplifies the processing required to make the decision.

  In general, the instrument may be for performing any type of biochemical analysis, such as analysis of molecules in a sample, eg, a polymer, or more specifically a polynucleotide.

  In one advantageous example, the biochemical analysis is sequencing of the polynucleotide in the sample, so that the output data includes sequence data representing the sequence of the polynucleotide.

  In another advantageous example, the analytical device can support a plurality of nanopores, using nanopores, for example, using electrodes that generate electrical signals at both ends of each nanopore from which output data is derived. Is operable to perform a biochemical analysis of the sample. In this case, again, the biochemical analysis may be polynucleotide sequencing, but the nanopore can be used to perform other types of biochemical analysis as well.

  In the second aspect of the present invention, specifically, electrodes are used to generate electric signals at both ends of each nanopore, and a signal processing circuit outputs output data in a plurality of parallel channels from the electric signals. The present invention relates to an apparatus for performing biochemical analysis of a sample using a nanopore, which is used to generate a slag. This type of device is known, for example, from WO-2009 / 077734. However, it is still desirable to optimize the efficiency of the instrument in generating output data.

According to a second aspect of the present invention, a module for performing a biochemical analysis is provided, the module comprising:
An analyzer that can support a plurality of nanopores, is operable to perform biochemical analysis of a sample using the nanopore, and comprises an electrode configured to generate an electrical signal at both ends of each nanopore; ,
A signal processing circuit configured to generate output data in a plurality of parallel channels representing a result of biochemical analysis from an electrical signal generated from the electrode;
The module can be controlled to change the performance of the module, and further includes a control unit operable to control the operation of the module based on the performance target.

  Such modules provide increased efficiency in generating output data from biochemical analysis because the operation of the module is controlled based on performance goals. Such performance targets and performance measurements may be the time to generate output data, the amount of output data, and / or the quality of the output data.

  The controller is configured to determine the performance measurement of the biochemical analysis at least once during the execution of the biochemical analysis, and to change the control in the operation of the module based on the performance measurement to meet the performance target. can do. This results in increased efficiency in generating output data from biochemical analysis because the operation of the module is intelligently controlled as follows. The control unit obtains a performance measurement value from the output data generated by the module, and changes the experimental parameters of the biochemical analysis based on the performance measurement value in order to satisfy the performance target. This determination and control may be performed repeatedly during the performance of the biochemical analysis, or even continuously. Examples of experimental parameters that can be changed include analyzer temperature, biochemical analysis electrical parameters, or output data sampling characteristics. Changes in these and other experimental parameters allow performance changes, such as changes in the amount, quality, and speed of output data. Typically, module performance can vary based on a variety of factors, so this dynamic operational control can effectively manage the overall device performance to meet the goal. This results in increased efficiency.

  For example, if the biochemical analysis is sequencing of polynucleotides in a sample, the instrument can detect, for example, pathogen detection in a broad background, plasma DNA, until a defined number of bases are sequenced. Analyzes at optimal performance until specific sequences are detected, such as detection of cancer mutations in, or for very long periods of time allowing the measurement of very rare polynucleotides, or without user guidance Can operate in many different ways, such as providing a pipeline of

  In U.S. Patent Application No. 61 / 170,729, a method for sensing a physical phenomenon is disclosed, the method comprising providing a sensor device comprising an array of sensor elements including corresponding electrodes comprising: Each sensor element is configured to output an electrical signal at the electrode with a changeable performance, depending on the physical phenomenon, each step and an electrical signal from one of the sensor elements Providing a detection circuit comprising a plurality of detection channels, wherein the number of sensor elements in the array is greater than the number of detection channels and selectively connecting the detection channels to the corresponding sensor elements And providing an acceptable performance based on the amplified electrical signal output from the detection channel. For selectively connecting the detection channel to each of the sensor elements to and controlling the switch component. In the second aspect of the present invention, the method disclosed in US Patent Application No. 61 / 170,729 can be appropriately omitted.

  Optionally, the module according to the second aspect of the invention may be operable as part of a cluster to perform a common biochemical analysis according to the first aspect of the invention.

  In general, the module may be for performing any type of biochemical analysis using nanopores. In one advantageous example, the biochemical analysis is sequencing of the polynucleotide in the sample, so that the output data includes sequence data representing the sequence of the polynucleotide.

According to a third aspect of the present invention, there is provided a module for performing biochemical analysis, comprising an electronic circuit part and a cartridge that can be removably attached to the electronic circuit part.
This cartridge
A sensor device capable of supporting a plurality of nanopores, operable to perform a biochemical analysis of a sample using the nanopore, and comprising an electrode configuration at each end of each nanopore;
At least one container for containing samples;
At least one reservoir for holding material for performing biochemical analysis;
A sample from at least one container and a fluid system configured to controllably supply material from the at least one reservoir to the sensor device;
The electronic circuit unit includes a drive circuit and a signal processing circuit configured to be connected to electrode components at both ends of each nanopore when the cartridge is attached to the electronic circuit unit, and the drive circuit performs biochemical analysis. The signal processing circuit is configured to generate output data representing the result of the biochemical analysis from the electrical signals generated from the electrode components at both ends of each nanopore. It is configured.

  The module has a structure in which parts and materials necessary for performing biochemical analysis are encapsulated in a cartridge separately from an electronic circuit unit including a drive circuit and a signal processing circuit. In particular, this module uses a nanopore to biochemically analyze a sample with at least one reservoir to hold the required material and a fluid system that can supply the material to the sensor device under appropriate control. It incorporates a sensor device that is operable to perform Since the cartridge can be removably attached to the electronic circuitry, the cartridge can be replaced for further sample analysis operations. This allows for efficient operation in biochemical analysis.

  Embodiments of the present invention will now be described by way of non-limiting example with reference to the accompanying drawings.

It is the schematic of a biochemical analyzer. It is a perspective view of the module in a biochemical analyzer. It is a perspective view of the cartridge which can be replaced in a module. It is sectional drawing of a part of sensor device in a cartridge. It is a top perspective view of a sensor device mounted on a PCB. It is a bottom perspective view of a sensor device mounted on a PCB. It is a perspective view of a module. It is the schematic of the electric circuit in a module. It is the schematic of a control part. FIG. 4 is a diagram of a detection channel. It is a perspective view from the top in the cartridge which has an alternative structure. FIG. 12 is a perspective view from below of the cartridge of FIG. 11 showing the well plate attached. FIG. 12 is a perspective view from below of the cartridge of FIG. 11 showing the well plate being separated. It is a cross-sectional perspective view in a well plate part. FIG. 6 is a top perspective view of a valve assembly incorporating a valve. FIG. 6 is a perspective view from below of a valve assembly incorporating a valve. FIG. 6 is a cross-sectional view of a valve assembly. FIG. 6 is a partial plan view from above the body of the valve assembly around the valve stator. It is a top view from the bottom in the rotor of a valve | bulb. FIG. 3 is a partial cross-sectional view of a body in a valve assembly and a well in a well plate. It is a top view from the bottom in the 2nd plate of a valve assembly. 1 is a perspective view of a valve assembly including a motor. FIG. It is a flowchart in the control process of an apparatus.

  First, an instrument for performing biochemical analysis using a nanopore that is a form of protein pore supported in an amphiphilic membrane will be described, but this does not limit the present invention.

  The device 1 is formed of a plurality of modules 2 connected to the data network 3 respectively. In this example, the network 3 is formed as a conventional local area network with each module 2 connected to a network switch 5 by a cable 4. In general, module 2 may be connected to any type of data network including a wireless network, a wide area network, and the Internet.

  Further, the network 3 may be equipped with an arbitrary type of storage device 6 such as NAS and an external computer 7 that is used for addressing the module 2 and may be a conventional computer having an HTTP browser.

  Due to the networked configuration of the equipment 1, depending on the location requirements, it can be used in a specific location, for example from a small number of modules 2 in a small laboratory or even from a single module 2 Any number of modules 2 can be provided, up to a large group of modules 2 in the sequencing center.

  Next, each module 2 will be described.

  As shown in FIG. 2, the module 2 has a replaceable cartridge 10 in the housing 11 of the module 2. The cartridge 10 forms an analyzer for performing biochemical analysis, as will be described next. The cartridge 10 has two alternative structures shown in FIGS.

  The cartridge 10 includes a main body 37 formed of molded plastic or the like. The main body 37 of the cartridge 10 carries the sensor device 14, which is an apparatus as described in detail in WO-2009 / 077734, which is incorporated herein by reference. Without being limited to most of the teachings herein, the sensor device 14 has the structure shown in the cross-section of FIG. 4, which includes a plurality of wells 21 therein. Each well 21 including a formed body 20 is a recess having a well electrode 22 disposed in each well 21. A number of wells 21 are provided to optimize the data collection rate. In general, there may be any number of wells 21, but only a few wells 21 are shown in FIG. In one example, the number of wells is 256 or 1024, but may exceed one, two, or three digits. The body 20 is covered by a cover 23 that extends over the body 20 and is hollow to define the chamber 24, and each well 21 is open in the chamber 24. A common electrode 25 is disposed in the chamber 23.

  The sensor device 14 is prepared to form an amphiphilic membrane 26 such as a lipid bilayer across each well 21 and to insert nanopores that are protein pores into the amphiphilic membrane 26. This preparation is achieved using the techniques and materials described in detail in WO-2009 / 077734, but can be summarized as follows. An aqueous solution is introduced into the chamber 24 to form an amphiphilic membrane 26 across each well 21 that separates the aqueous solution in the well 21 from the remaining portion of the aqueous solution in the chamber 24. The protein pores are supplied into the aqueous solution, for example, by being introduced into the aqueous solution before or after the aqueous solution is introduced into the chamber 24, or by depositing on the inner surface of the chamber 24. The protein pore naturally enters the amphiphilic membrane 26 from its aqueous solution.

  Protein pores are examples of nanopores and can be used as follows to perform biochemical analysis. For a given well 21 that is optional, when an amphiphilic membrane 26 is formed and a protein pore enters the amphiphilic membrane 26, the well 21 is a probabilistic physics between the molecular compound and the protein pore. It can be used as a sensor element that senses an interaction that is an event, in that the output electrical signal at both ends of the amphiphilic film 26 causes this interaction to cause a characteristic change in the amphiphilic film 26. This is because they depend on these interactions. For example, in general, an interaction will occur between a protein pore and a specific molecular compound (analyte), which modulates the flow of ions through the protein pore and causes the current to flow through the pore. It brings about characteristic change. The molecular compound may be a molecule, such as a DNA base, or may be part of a molecule. Thus, this interaction appears as a characteristic event of the electrical signal at both ends of the protein pore in each amphiphilic membrane 26.

  Further details regarding the nature of the sensor device 14 and biochemical analysis performed by the sensor device 14 are described below towards the end of the specification.

  The electrical signal can be detected as a signal between the well electrode 22 and the common electrode 25 and can then be analyzed to produce output data representing the results of the biochemical analysis. From the protein pores in the amphiphilic membrane 26 of different wells 21, different electrical signals are obtained, each providing output data for a different channel.

  A wide variety of biochemical analyzes can be performed. One such biochemical analysis is polynucleotide sequencing. In this case, the electrical signal is modulated differently for different bases, thereby enabling base identification.

  The main body 37 of the cartridge 10 encapsulates parts and materials necessary for performing biochemical analysis, and the sensor device 14 can be automatically prepared. For this purpose, the cartridge 10 carries a reservoir 30 containing a sufficient amount of necessary materials such as buffers, lipids, protein pores (in solution), pretreatment agents (if necessary), and samples. This allows a number of “refreshes” in the analyzer. Thus, the cartridge 10 is completely self-contained in that all reagents and other materials necessary for biochemical analysis are present and can be used for sample preparation. The cartridge 10 is equipped with a waste reservoir 35 for disposing of waste from the sensor device 14. This waste reservoir 35 is shown in FIG. 11 but is not visible in FIG. 3 because it is below the body 37 in the structure of FIG.

  The main body 37 of the cartridge 10 is equipped with a fluid system 31 for supplying fluid from the reservoir 30 to the sensor device 14. The fluid system 31 includes a supply channel 32 and an inlet pump 33 for pumping fluid from the reservoir 30 to the sensor device 14. In addition, the fluid system 31 includes an output pump 34 for pumping fluid out of the sensor device 14 via an outlet channel 36 connected to a waste reservoir 35 for disposal of the fluid. Pumps 33 and 34 may be syringe pumps (such as CH-7402 supplied by Hamilton Company, Via Crus 8, Bondaduz, GR, Switzerland) depending on the required volume and flow rate.

  The fluid system also includes a selector valve 45 disposed in the supply channel 32 between the inlet pump 33 connected to the reservoir 30 and the output pump 34. The selector valve 45 selectively connects the sensor device 14 to the reservoir 30 or the waste reservoir 35. The waste reservoir 35 is open to the atmosphere.

  One of the reservoirs 30 holds the lipid, and the fluid system 31 supplies the lipid to the sensor device 14 like any other material. As an alternative to supplying lipid, the supply channel 32 of the fluid system 31 is connected to the sensor device 14 via a lipid assembly holding the lipid so that the fluid flowing into the sensor device 14 is free of lipid. And the lipid may be introduced into the sensor device 14.

  Therefore, as described above, the pumps 33 and 34 operate to control the flow of fluid to form the amphiphilic film 26 across the respective wells 21, and the nanopores that are protein pores are converted into the amphiphilic film. Sensor device 14 may be prepared for insertion into 26.

  In the structure of FIG. 3, the main body 37 of the cartridge 10 is equipped with a container 44 for containing a sample. In use, the sample is introduced into the container 44 before the cartridge 10 is loaded into the module 2. After the sensor device 14 is prepared, the fluid system 31 is controlled to supply a sample from the container 44 to the sensor device 14 for biochemical analysis.

  In the structure of FIG. 11, the cartridge 10 can contain a plurality of samples as follows. As shown in FIG. 12, the body 37 of the cartridge 10 is configured to allow attachment of the well plate 100. In particular, the main body 37 has a pair of clips 101 protruding from the lower surface of the main body 37, and the well plate 100 is clipped by pressing the well plate 100 against the clip 101 in the direction of the arrow in FIG. 101 can be attached.

  As shown in FIG. 14, the well plate 100 is of a standard structure and forms a plurality of wells 102 that open the flat top surface 103 of the well plate 100. In this example, the well plate 100 has 96 wells 102, but can generally have any number of wells 102. The well 102 is used as a container for holding each sample. In use, a sample is introduced into each well 102 before the well plate 102 is attached to the cartridge 10 and before the cartridge 10 is loaded into the module 2. Well plate 102 can be filled with samples using well-known plate-based parallel operation techniques that are inherently efficient. Since the well plate 100 is a separate part from the main body 37 of the cartridge 10, it is easily filled before installation, thereby facilitating filling of the well 102. More generally, similar advantages may be achieved by replacing the well plate 100 with any other type of container part comprising multiple containers, which may be wells or closed containers.

  After the sample is introduced, the well plate 100 is attached to the cartridge 10 with the flat top surface 103 pressed against the body 37 in order to encapsulate the well plate 100 in the cartridge 10. Next, the cartridge 10 is loaded into the module 2.

  The fluid system 31 is configured to selectively supply a sample from the well 102 to the sensor device 14 using the valve 110. Valve 110 is a rotary valve and will be described next.

  The valve 110 is formed in a valve assembly 111 shown in FIGS. 15-21 that is incorporated into the body 37 of the cartridge 10.

  The valve 110 includes a stator 112 and a rotor 113. The stator 112 is provided on a main body 120 formed by a first plate 121, a second plate 122, and a third plate 123, and these plates are interface contacts between the first plate 121 and the second plate 122. The surface 124 and the interface contact surface 125 between the first plate 122 and the second plate 123 are fixed to each other.

  The rotor 113 is rotatably mounted on the stator 112 in order to rotate around the rotation axis R. A rotor 113 that includes a bearing stub 114 that is attached to a bearing recess 115 formed in the stator 112 provides a bearing for mounting for rotation. In particular, the bearing stub 114 has a length selected to provide a clearance between the end of the bearing stub 114 and the first sheet 121. The second sheet 122 has an annular boss 126 protruding toward the first sheet 121 and the stator 113 around the bearing recess 115, and the second sheet 123 has a circular hole 127 into which the annular boss 126 is fitted. Have

  In addition, a disk portion 116 having a cylindrical outer surface 117 is formed on the stator 112 and rides on the inside of the stator 112, in particular the annular wall 118 protruding from the third plate 123 to the outside of the circular hole 127. The rotor 113 provides a bearing for mounting for rotation. Alternatively, there may be a gap between the disc portion 116 and the annular wall 118.

  The stator 112 and the rotor 113 have an annular interface contact surface 130 extending perpendicular to the rotation axis R. The interface contact surface 130 is provided as follows. The contact surface 130 of the rotor 113 is formed by the lower surface of the disc portion 116. The lower surface of the disc portion 116 extends perpendicularly to the rotation axis R, partially overlaps the annular boss 126 of the second plate 122, and partially overlaps the third plate 123 outside the hole 127. Yes. Accordingly, the contact surface 130 of the stator 112 is formed by adjacent portions of the upper surface of the annular boss 126 in the second plate 122 and the upper surface of the third plate 123 that are in the same plane.

  Sealing of the interface contact surface 130 of the stator 112 and the rotor 113 is facilitated by applying a load along the rotation axis R between the stator 112 and the rotor 113. This is realized by a bias configuration configured as follows in order to bias the rotor 113 against the stator 112. A clamp ring 131 is attached to the stator 113 and in particular is screwed to the annular wall 118. A disc spring 132 is disposed between the clamp ring 131 and the rotor 112 and meshes with the clamp ring 131 and the rotor 112. The disc spring 132 provides an elastic bias between the stator 112 and the rotor 113, but may be replaced with another type of elastic biasing element.

  The contact surface 130 of the stator 112 is configured as shown in FIG. FIG. 18 is a plan view of the stator 112 without the clamp ring 131. In particular, a plurality of inlet ports 133 are formed on the contact surface 130 of the stator 112, and these inlet ports 133 are arranged around the rotation axis R in a circular shape. The suction port 133 is equally spaced apart from the gap at the lowest position in FIG. In particular, these inlet ports 133 are formed on the upper surface of the annular boss 126 in the second plate 122 facing the contact surface 130 of the rotor 113.

  A collection chamber 134 is formed on the contact surface 130 of the stator 112. The collection chamber 134 is formed as a groove in the upper surface of the third plate 122 facing the contact surface 130 of the rotor 113. The collection chamber 134 extends in an annular shape around the rotation axis R to the outside of the inlet port 133 and is aligned with the inlet port 133 by an angle. That is, the gap is aligned around the rotation axis R by the angle of the gap of the suction port 133.

  The stator 112 further includes an outlet port 135 that is formed in the lower surface of the collection chamber 134 and communicates with the collection chamber 134.

  The rotor 113 is provided with a passage 136 formed as a groove in the contact surface 130 of the rotor 113. This passage 136 extends radially from the position of the inlet port 133 to the position of the collection chamber 135. Accordingly, the passage 136 can communicate with one arbitrary inlet port 133 according to the rotational position of the rotor 113. Depending on the rotation of the rotor 113, different inlet ports 133 can be selected. Since the collection chamber 134 is angularly aligned with the inlet port 133 at all rotational positions where the passage 136 communicates with the inlet port 133, the passage 136 is also in communication with the collection chamber 134, thereby selecting the selected inlet port. 133 is connected to the outlet port 135. Accordingly, the individual intake port 133 is selectively connected to the discharge port 135 by the rotation of the rotor 136.

  When the rotor 133 is aligned with the gap portion of the inlet port 133 and the gap portion of the collection chamber 134, the passage 136 is pressed against the contact surface 130 of the stator 112 and closed, and thus the valve 110 is closed. However, as an alternative, the inlet port 133 may be connected to eliminate the gap portion, so that the inlet port is arranged in a complete ring and the valve 110 is not closed.

  As an alternative to forming the collection chamber 134 on the contact surface 130 of the stator 112, a similar operation is to replace the collection chamber 134 as a groove in the contact surface 130 of the rotor 113 that opens into the passage 136. It can also be realized by forming.

  In order to enable positioning of the rotor 112, the contact surface 130 of the stator 112 has a circular array of pits 137 having the same pitch as the suction port 133, and the contact surface 130 of the rotor 113 fits into these pits 137. There is a rounded protrusion (pip) 138. The protruding portions 138 may be pushed out of the pits 137 during rotation of the rotor 112, but the protruding portions 138 are each in communication with a corresponding inlet port 133 and are arranged with passages 136. A stage that is aligned to hold the rotational position of the rotor 112 in a rotational position that is attached to, or that places a passage 136 over the gap portion of the inlet port 133 and the gap portion of the collection chamber 134; The rotation position of the rotor 112 is aligned with one of the attached rotation positions.

  The size of the valve 110 is minimized by arranging the inlet ports 133 as close as possible to each other, but the size of the gap portion of the inlet port 133 is set so that the inlet port 133 extends around the small ring portion. By increasing the number, the same operation can be realized. In this case, the length of the collection chamber 134 is correspondingly shortened and can extend to a short part of the ring.

  The body 120 defines a channel that connects the well 102 of the well plate 100 to the inlet port 133 as follows.

  The first plate 121 is disposed on the lower surface of the cartridge 10 at a position where the well plate 100 is attached, and has an array of nozzles 140 protruding outward. The array of nozzles 140 has the same spacing as the wells 102 for alignment with the wells 102 of the well plate 100. Thus, when the well plate 100 is attached to the cartridge 10, each nozzle 140 protrudes into the corresponding well, as shown in FIG. Each nozzle 140 includes a through hole 141. The through hole 141 extends through the nozzle 140 and the first plate 121 to the contact surface 124 of the first plate 121 to form a part of the channel for the well 102.

  The nozzle 140 extends into the well 102 a sufficient distance that the end of the nozzle 140 sinks below the surface of the sample 142 in the well 102. In this way, the sample 142 effectively confines the nozzle 140. This avoids the need for a hermetic seal between the well plate 100 and the first plate 121.

  The contact surface 124 of the second plate 122 is formed with a set of grooves 143 that form part of the channel for each well 102. Each groove 143 communicates at one end with a through hole 141 extending through the nozzle 140 and the first plate 121. As shown in FIG. 20, the groove 143 extends from the nozzle 140 to the stator 112, and in particular, extends from the discharge port 133 to the annular boss 126 on the opposite side of the second plate 122. The remainder of the channel is formed by holes 144 extending from respective grooves 144 in the contact surface 124 of the second plate 122 through the annular boss 126 of the second plate 122 to the corresponding inlet port 133. Yes.

  The main body 120 also defines a channel connected to the outlet port 135 as follows. The third plate 123 has a through hole 145 shown in a dotted outline in FIG. The through hole 145 extends from the outlet port 135 through the third plate 123 to the contact surface 125 of the third plate 123 to form a part of the channel. The remainder of the channel is formed by a groove 146 in the contact surface 125 of the third plate that extends away from the through hole 145. As shown in FIG. 17, the groove 146 is a dosing pump 147 operable to pump a sample from the well 102 selected by the rotational position of the valve 110 to the sensor device 14 via the valve 110. It extends to.

  The first plate 121, the second plate 122, and the third plate 123 are formed from any suitable material that allows sealing for the channels defined between the contact surfaces 124 and between the contact surfaces 125. be able to. Suitable materials include PMMA (polymethyl methacrylate), PC (polycarbonate), or COC (cyclic olefin copolymer). The first plate 121, the second plate 122, and the third plate 123 can be sealed by any suitable technique such as ultrasonic welding, laser welding, or adhesion. PMMA is particularly effective because PMMA diffusion bonding can be used. The first plate 121, the second plate 122, and the third plate 123 may be injection molded.

  Similarly, the rotor 113 can be formed from any suitable material that allows sealing and sufficiently low friction against rotation. One suitable material is PTFE (polytetrafluoroethylene), which can be machined with a cross section made from an elastomer (eg, silicone) that allows compression. PTFE can reduce the torque required for rotation and has good sealing characteristics. The elastomer allows it to still rotate even when the rotor 112 is fixed. Alternatively, the rotor 113 may be made from an injection moldable material such as FEP (fluorinated ethylene propylene) or UHMWPE (ultra high molecular weight polyethylene).

The valve 110 is not limited to use in the cartridge 10 and may be used for other purposes. Since the valve 110 may be used for flow in the opposite direction from the outlet port 135 to the inlet port 133, the inlet port 133 may be more commonly referred to as the first port, Port 135 may be referred to as a second port. The valve 110 is particularly suitable as a small element for handling low volumes of fluid, in which case the inlet port 133, passage 136, collection chamber 134, and outlet port 135 have a cross-sectional area of 10 mm 2 or less, preferably Has a cross-sectional area of 1 mm 2 or less.

  The rotor 113 is operated by a motor 150 shown in FIG. The rotor 113 has a coupling element 152 protruding upward from the rotor 113, and a drive shaft 151 on which a gear wheel 153 is mounted is fitted to the coupling element 152. The motor 151 has an output shaft 154. The output shaft 154 implements a gear profile 155 that engages the gear wheel 153 to drive the rotation of the drive shaft 151 and thereby the rotor 113. The drive shaft 151 includes an encoder wheel 156, and the position of the encoder wheel 156 is detected by a sensor 157. The motor 150 is driven based on the output of the sensor 157 and allows the rotor 113 to rotate to select the desired inlet port 133.

  The fluid system 31 is controlled to sequentially perform biochemical analysis on a continuous sample. The sensor device 14 is prepared and then the fluid system 31 is controlled to supply the sample from one of the wells 102 to the sensor device 14. After the biochemical analysis is performed, the sensor device 14 is emptied and washed to remove the sample. Next, the sensor device 14 is prepared again and the fluid system 31 is controlled to supply the sample from the next well 102 by rotating the rotor 112 of the valve 110.

  Next, a specific example relating to a method using the cartridge 10 having the structure of FIG. 11 will be described. The materials used are those described in detail in WO-2009 / 077734.

  First, a pretreatment coating is applied to modify the surface of the body 20 of the sensor device 14 surrounding the well 21 in order to increase the affinity for amphiphilic molecules. The required amount of pretreatment agent is a hydrophobic fluid, and an organic substance, usually an organic solvent, is fed by an inlet pump 33 by a supply channel 32 to fill the chamber 24 covering the body 20 and well 21. Is pulled out from the reservoir 30 and supplied. Excess material is discharged to the waste reservoir 35.

  Various cartridges 10 can be used to discharge excess pretreatment agent. An example is the inlet pump 33 applying a gas flow through the supply channel 32 and the chamber 24 to transfer fluid through the outlet channel 36 to the waste reservoir 35. Alternatively, the pretreatment agent may be supplied from the inlet pump 33 by the required amount of gas behind the pretreatment agent, and the excess amount of pretreatment agent passes through the chamber 24 in a single operation. It enters the outlet channel 36 and is discharged from the outlet channel 36 to the waste reservoir 35. The gas continues to flow through chamber 24 for flowing solvent vapor from the fluid system until the final pretreatment coating is achieved. In a further variation, this last step may be accomplished more quickly by gas flow or warming of the body 20.

  After applying the pretreatment coating, an aqueous solution containing amphiphilic molecules is flowed over the body 20 so as to cover the well 21. In order to fill the chamber 24 covering the body 20 and the well 21, the required amount of aqueous solution is withdrawn from the appropriate reservoir 30 and supplied by the inlet pump 33 by the supply channel 32.

  The formation of the amphiphilic film 26 is directly performed by amphiphilic molecules. Alternatively, this formation can be improved if a multi-pass technique is applied where the aqueous solution covers the recess well 21 at least once and removes the cover before the recess well 21 is finally covered. The The aqueous solution containing amphiphilic molecules may be withdrawn directly from the reservoir 30, or alternatively, the aqueous solution containing amphiphilic molecules may be passed through the lipid assembly in the flow path of the supply channel 32 instead of the above approach. It may be made by sending it to the chamber 24.

  In the first example, multi-pass at the solution air interface may be achieved by reversing the flow in the chamber 24. The flow to and from the reservoir 30 is stopped by the operation of the selector valve 45 and the operation of the outlet pump 34 causes the solution containing amphiphilic molecules to pass through the supply channel 32 into the chamber. Withdrawn from 24, air is withdrawn from the outlet channel 36 to the waste reservoir 35. The direction of the outlet pump 34 is reversed and the solution is returned to the other side of the well 21 which is filled with the solution.

  The formation of the amphiphilic film 26 is observed by monitoring the resulting electrical signal between the electrodes 22 and 25 when a potential is applied to the formation resulting in a resistance barrier and a reduction in the measured current. be able to. If the amphiphilic film 26 cannot be formed, it is easy to perform another pass at the aqueous solution air interface.

  Alternatively, in the second example, multi-pass at the solution air interface can be achieved by unidirectional flow by including air slugs during solution delivery. In this second example, an aqueous solution containing amphiphilic molecules is withdrawn from the reservoir 30 into the inlet pump 33 and then pumped to the supply channel 32 by operation of the backflow prevention valve. The air slag changes the position of the selector valve 45 to stop the aqueous solution of amphiphilic molecules, and it is discarded by the operation of another inlet pump 33 (since the waste reservoir 35 is opened to the atmosphere). It may be formed by introducing the required amount of air into the channel behind the solution from the material reservoir 35. The selector valve 45 is returned to the previous position and an aqueous solution of further amphiphilic molecules is pumped forward. The inlet pump 33 moves the solution forward through the supply channel 32 to the chamber 24 and through the outlet channel 36 to the waste reservoir 35 so that an aqueous stream of amphiphilic molecules, including air slag, is added to the well 21. Passed over. This process is repeated to achieve the desired number of passes.

  Excess amphiphilic molecules are removed from the chamber 24 by flowing the aqueous buffer from the reservoir 30 by operation of the inlet pump 33. Multiple amounts of aqueous buffer are passed through the outlet channel 36 through the chamber 24 for delivery to the waste reservoir 35.

  Preparation of the sensor device 14 is completed by the flow of an aqueous solution containing a membrane protein such as alpha hemolysin or a variant thereof from the reservoir 30 by operation of the inlet pump 33 into the chamber covering the layer 26, which flow is It allows membrane proteins to naturally enter the layer 26 of amphiphilic molecules after a certain time.

  In an alternative approach, the membrane protein may be dried and incorporated. In this case, the position of the selector valve 45 used to rehydrate the membrane protein is changed before using the inlet pump 33 to flow the resulting solution into the chamber above the layer 26. This allows the aqueous solution to be sent from the appropriate reservoir 30 to the second reservoir 30 containing the membrane protein in a dry form by means of the inlet pump 33 via the supply channel 32.

  The insertion process into layer 26 can be observed by monitoring the resulting electrical signal between electrodes 22 and 25 when a potential is applied to the insert resulting in increased ionic conduction and increased measurement current. it can.

  At the end of the insertion period, the aqueous solution containing the membrane protein is removed from the supply channel 32 and the chamber 24 by flushing the aqueous buffer from the reservoir 30 by operation of the inlet pump 33. Multiple amounts of aqueous buffer are passed through the outlet channel 36 through the chamber 24 for delivery to the waste reservoir 35.

  As soon as the preparation of the sensor device 14 is complete, the analysis of the sample contained in the well plate 100 can begin. The rotary valve 110 is configured to allow fluid to contact the first inlet port 133. The selector valve 45 is positioned to stop flow from the fluid reservoir 30 and the outlet pump 34 operates to withdraw sample material from the sample well 102. The rotary valve 110 is repositioned to direct the flow towards the supply channel 32 and to fill the chamber 26 to cover the membrane layer 26 in the sensor system. When the analysis is complete, the selector valve 45 causes the aqueous buffer flow from the inlet pump 33 to draw the sample from the supply channel 32, rotary valve 110, and chamber 24 to prevent contamination of the next sample. The waste reservoir 35 is positioned through the outlet channel 36 so that it can be flushed with a plurality of buffers.

  The selector valve 45 is positioned to stop flow from the fluid reservoir 30 and the valve 110 is repositioned to make a fluid connection to the next sample well 102 in the well plate 100. This process is repeated for all samples.

  Once all samples have been analyzed, any cartridge 10 can be disposed of. Alternatively, since the well plate 100 is an independent part, the well plate 100 can be removed and disposed of and replaced with a new well plate 100 loaded with an unused sample. Such use of the well plate 100 as a disposable part allows the cartridge 10 to be reused.

  The sensor device 14 is formed in a chip mounted on a printed circuit board (PCB) 38 and is electrically connected to the PCB 38. The electrical contacts of the PCB 38 are configured as edge connector pads for electrical connection with the sensor device 14. When the cartridge 10 is inserted into the module 2, the contact 39 makes an electrical connection to the remainder of the electrical circuit in the module 2, described below. Three alternative designs for sensor device 14 and PCB 38 are as follows.

  In the first possible design shown in FIGS. 5 and 6, the sensor device 14 is disclosed in WO-2009 / 077734 as an array of electrodes embedded in wells made on silicon. The wells are made in a suitable inert layer on the top surface of the silicon, and the electrical connections are made at the bottom of the silicon substrate using solder bumps bonded to the through-wafer vias, PCB38. Made to. The PCB provides an equivalent number of connections to two (or generally any number) application specific integrated circuits (ASICs) 40 that are similarly bonded to the opposite side of the PCB 38. The ASIC 40 includes some of the components in the electrical circuit of module 2, described below. The ASIC 40 may include components in processing circuitry for processing electrical signals from the sensor device 14, such as amplifiers for providing digital outputs, sampling circuits, and analog-to-digital converters (ADCs). The digital output is supplied from the contact portion 39 using a suitable interface such as a low voltage differential signal system (LVDS), and the digital output can be output from the sensor device 14. Alternatively, the output signal may be provided in an amplified analog form and an ADC may be provided in the module. The ASIC 40 receives power via, for example, a contact point for setting and monitoring a function parameter including a current measurement sample rate (1 Hz to 100 kHz), a capacitor for integration, a bit resolution, an applied bias voltage, and the like. It may also include several components in the control circuit that controls the command.

  A possible second design is to form the sensor device 14 as a simple electrode array chip made on silicon, mounted on the PCB 38 and wire bonded to the contacts 39. This connection can be connected to the electrical circuit as a series of discrete channels, or can be connected to the electrical circuit using a suitable ASIC. Such an ASIC may be, for example, a conventional electronic readout chip (such as FLIR ISC9717) provided by FLIR Systems as an arrayed electrode measurement device.

  A possible third design is to make the sensor device 14 and the ASIC 40 as one device mounted on the PCB 38.

  Next, the configuration of the module 2 will be described with reference to FIG. 7 showing the module 2 with the housing 11 removed to show the physical layout. Module 2 includes an internal board 50 and an embedded computer 51 connected to each other by a PCI data collection module 52, both of which comprise the electrical circuitry described below. The internal board 50 contacts the contact part 39 of the cartridge 10 when inserted into the module 2.

  The embedded computer 51 may be a conventional computer and includes a processing unit and a storage unit. The embedded computer 51 allows the module 2 to connect to the network 3, thereby turning the module 2 into a stand-alone network device and, further, as described below, a plurality of modules 2 can be clustered. It includes a network interface 53 that provides a “hook” that allows it to be operated, managed, and controlled. For example, the embedded computer 51 can operate a reduced scale operating system (for example, Linux (registered trademark)) and an application that performs various functions described below. A complete development kit for such embedded systems is available on the market.

  Module 2 includes a loading device 54 for automatically loading cartridge 10 into module 2 and automatically removing cartridge 10 from module 2. The loading device 54 may be a uniquely developed device driven by a high-precision stepping motor, for example.

  Module 2 also includes a microcontroller 58 and FPGA 72 mounted on internal board 50 that controls the various components of module 2 as described below.

  The module 2 includes a fluid operating unit 60 that is mounted on the internal board 50 and controls the fluid system 31.

  Furthermore, the module 2 comprises a temperature control element 42 arranged to control the temperature of the cartridge 10, in particular the sensor device 14. The temperature control element 42 may be, for example, a Peltier temperature controller, such as a 32 watt single stage thermoelectric module (eg, Ferrotec Corp, 33 Constition Drive, Bedford NH 03110 USA—Part No. 9500/071 / 060B). The temperature control element 42 may be mounted, for example, under the cartridge 10 and is therefore not visible in FIG. The temperature control element 42 may be considered as a part of the analyzer mainly formed by the cartridge 10 or may be mounted on the cartridge 10.

  Finally, module 2 includes a display 55 for displaying basic operational status information, a power source 56 for supplying power to the various components of module 2, and a cooler assembly 57 for cooling module 2. including.

  Next, an electric circuit provided by the internal board 50 and the embedded computer 51 will be described with reference to FIGS. This electrical circuit has two main functions, namely a signal processing function and a control function, thereby acting as both a signal processing circuit and a controller for the module 2.

  The signal processing function is distributed between the internal board 50 and the embedded computer 51, and is provided as follows.

  The sensor device 14 is formed in the ASIC 40 on the PCB 38 of the cartridge 10 and is connected to a switch component 62 that is controlled by a control interface to the ASIC 40. The switch configuration unit 62 is configured to selectively connect the well electrode 22 of the sensor device 14 to a corresponding contact for provision to the detection channel 65 belonging to the signal processing function. Is more than the detection channel. The switch component 62 is configured and operates as described in detail in US Patent Application No. 61 / 170,729, which is incorporated herein by reference.

  Alternatively, the switch configuration unit 62 may be provided and controlled separately from the ASIC 40 as a stand-alone functional block between the sensor device 14 and the detection channel 65, and the detection channel 65 is provided by FLIR Systems. May be provided in a readout chip (such as FLIR ISC9717).

  The ASIC 40 provides an array of detection channels 65 each arranged as shown in FIG. 10 to amplify an electrical signal from one of the well electrodes 26. Accordingly, the detection channel 65 is designed to amplify a very small current with sufficient resolution to detect characteristic changes caused by the interaction of interest. Furthermore, the detection channel 65 is designed with a sufficiently high bandwidth to provide the time necessary to detect each such interaction. Therefore, these constraints require expensive and sensitive parts.

  The detection channel 65 includes a charge amplifier 66, and the charge amplifier 66 is configured as an integrating amplifier by a capacitor 67 that connects between the inverting input of the charge amplifier 66 and the output of the charge amplifier 66. The charge amplifier 66 integrates the current supplied from the well 21 to the charge amplifier 66 and provides an output representative of the charge supplied during successive integration periods. Since the integration period is a constant duration, the output signal represents a current that is sufficiently long to provide sufficient resolution to monitor events occurring in the well 21 connected to the charge amplifier 66. short. The output of the charge amplifier 66 is supplied to the sample and hold stage 70 through the low-pass filter 68 and the programmable gain stage 69. The sample and hold stage 70 samples the output signal from the charge amplifier 66 and samples it. Operates to generate a current signal. This output current signal is supplied to the ADC 71 and converted into a digital signal. A digital signal from each detection channel 65 is output from the ASIC 40.

  The digital signal output from the ASIC 40 is supplied from the PCB 38 of the cartridge 2 to the field programmable gate array (FPGA) 72 provided on the internal board 50 of the module 2 via the contact point 39. The FPGA 72 includes buffers arranged to buffer digital signals from the respective detection channels 65 prior to supply to the embedded computer 51 via the PCI data collection module 52.

  In an alternative configuration, the digital output from the detection channel is provided from a read chip located on the internal board 50 of the module 2 and fed to the FPGA 72.

  The embedded computer 51 is configured as follows to process the digital current signal from each detection channel 65 as follows. The PCI data collection module 52 controls the transfer of the digital current signal from the FPGA 72 to the embedded computer 51 where the digital current signal is stored as digital data.

  Thus, the digital data stored in the embedded computer 51 is the measured electrical current from each detection channel 65, which is the current measured by each well electrode 22 relative to the nanopore in the corresponding well amphiphilic membrane 26. Raw output data that is signal data representing a signal. The current from each nanopore is in the channel of the measured electrical signal. This raw output data is processed by a processing module 73 that includes a pipeline 74 for each channel. This processing module 73 is implemented by software executed by the embedded computer 51.

  The nature of the signal processing performed in each pipeline 74 in the processing module 73 is as follows. Pipeline 74 processes the raw output data representing the measured electrical signal to produce output data representing the results of the biochemical analysis for the corresponding channel. As explained above, the interaction between the nanopore and the sample causes a change in current characteristics, an identifiable event. For example, an analyte passing through a nanopore can reduce the current by a specific amount. Accordingly, the pipeline 74 detects these events and generates output data that is event data representing these events. An example of such a process is disclosed in WO2008 / 102120, which is incorporated herein by reference. In the simplest case, the output data, which is event data, can only represent that an event has occurred, but more generally includes other information about the event such as the size and time of the event.

  Furthermore, the pipeline can classify events and the output data can represent the classification of events. For example, nanopores can interact with different analytes in the sample and cause different modulations in the electrical signal. In this case, pipeline 74 sorts the analyte based on the modulated electrical signal. An example of this is that nanopores can interact with polynucleotide bases, where each base modulates the electrical signal differently. For example, a base that passes through a nanopore can reduce the current by an amount that characterizes the base. In this case, pipeline 74 classifies the event by identifying the base from the modulation of the electrical signal. Thus, biochemical analysis is the sequencing of a polynucleotide in a sample and the resulting output data is sequence data representing the sequence of the polynucleotide. This is sometimes referred to as “base calling”.

  Further, the pipeline 74 generates output data that is quality data representing the quality of the output data representing the result of the biochemical analysis. This may represent the probability that an event is detected and / or classified incorrectly.

  The output data may be represented in any suitable format. In the case of polynucleotide sequencing, sequence data and output data, which is quality data, may be represented in a FASTQ format, which is a conventional text-based format for nucleotide sequences and an associated quality score.

  All output data is stored in the embedded computer 51, and part or all of the output data may be transferred via the network 3 and stored in the storage device 6. Typically this includes at least output data (eg, sequence data) representing quality of the event and quality data, which is a relatively small amount compared to the raw output data representing the measured electrical signal. This is because it is data. Furthermore, event data and / or output data, which is raw data representing measured electrical signals at both ends of each nanopore, may be transferred and stored in response to a user request.

  The processing module 73 can also derive and store quality control metrics representing the parameters of the biochemical analysis itself.

  Various signal processing performed by the pipeline 74 may be performed by the internal board 50 before data is transferred to the embedded computer 51. This approach belongs to a unique use for a number of channels and the FPGA 72 can be particularly adapted to this type of task.

  Next, a control function unit configured to control the operation of the module 2 will be described. This control function part is distributed in the internal board 50 and the embedded computer 51, and is provided as follows.

  The control function unit includes a controller 58 such as a Cortex M3 microcontroller provided on the internal board 50. The controller 58 controls the operation of all parts in the analyzer 13. The controller 58 is configured to send commands to the pumps 33 and 34 of the fluid system 31 and other components that read data in advance by standard protocols and through low-level device drivers. . The status information is stored based on the error code obtained from the driver.

  The controller 58 is itself controlled by a control module 80 implemented in the embedded computer 51 by software executed on the embedded computer 51. The control module 80 communicates with the controller 58 via the RS232 interface 81. The control module 80 controls the controller 58 as follows so that the control module 80 and the controller 58 operate together to form the control unit of the module 2.

  The controller 58 controls the loading device 54 to load and remove the cartridge 10. At the time of loading, the controller 58 detects that appropriate electrical contact is made between the contact portion 39 and the internal board 50.

  The controller 58 controls the fluid actuator 60 to control the fluid system 31 to prepare the sensor device 14.

  During this preparation, the control module 80 uses, for example, analytical techniques disclosed in WO-2008 / 102120, which is incorporated herein by reference, to detect that the preparation has been performed correctly. The electrical signal output from the sensor device 14 can be monitored. Typically, the control module 80 will determine which well 22 has been set up correctly at the start of operation. This may include sensing double layer quality, electrode quality, pore occupancy, and even sensing whether the nanopore is in an active state after sample detection.

  Based on this monitoring, the controller 58 connects the detection channel 65 to the well electrode 26 in the well 22 of the sensor device 14 with acceptable performance by the method disclosed in detail in US patent application 61/170729. The switching controller 63 is controlled to cause the switch configuration unit 62 to do so.

  In the case of polynucleotide sequencing, the control module 80 has any modifications to the nanopore that may be required to process and measure DNA, such as the addition of exonuclease enzymes, cyclodextrin adapters, Alternatively, those conditions can also be sensed.

  The controller sets the following experimental parameters:

  The controller 58 controls a bias voltage source 59 that supplies a bias voltage to the common electrode 25. In this way, the controller 58 controls the bias voltage across each nanopore. The controller 58 controls the temperature control element 42 in order to change the temperature of the analyzer 13. The controller 58 can control the operation of the ASIC 40 to change sampling characteristics such as the sampling rate, the integration and reset periods of the capacitor 67, and the resolution of the resulting signal.

  The controller 58 may perform the above control functions and other experimental parameters via the FPGA 72. In particular, the control of the ASIC 40 is performed via the FPGA 72.

  When the sensor device 14 is correctly prepared, the controller 58 controls the analyzer 13 to introduce a sample and perform biochemical analysis. Next, in order to generate output data representing the analysis, an electrical signal is output from the sensor device 13, and the electrical signal is processed by the processing module 73, so that biochemical analysis is performed.

  As described further below, the control module 80 has local performance goals derived based on the inputs described below. This local performance goal represents the desired performance for the operation of module 2. This performance goal may relate to any combination of the upper time limit for generating output data, the amount of output data to be generated, or the quality of the output data to be generated, as required by biochemical analysis.

During operation, the control module 80 determines performance measurements in the biochemical analysis from the output data, but these performance measurements are of the same nature as the local performance target, i.e., the upper time limit during which the output data is generated. The amount of output data to be generated, or the quality of the output data to be generated. The control module 80 controls the controller 58 to control the analyzer 13 to meet the performance goal based on the performance measurement. This is done by starting and ending operation in the analyzer and / or changing operating parameters. In order to meet local performance goals, the controller 58 controls the following operating parameters that affect the speed and quality of data collection.
1) A temperature control element 42 that changes the temperature of the analyzer 13. For example, when sequencing an enzyme that sequentially supplies bases through nanopores, the temperature control element 42 may change the sensor device 14 by changing, for example, the speed of movement of molecules through the nanopore and / or the rate of treatment by the enzyme. Affects the biochemical analysis performed in In general, increasing the temperature increases the data collection rate but decreases the quality, and vice versa.
2) A bias voltage source 59 that changes the bias voltage across each nanopore. This is an electrical parameter in biochemical analysis that affects performance and can be modified to change speed and quality, or the nanopore can be targeted to focus on high quality measurements for a particular analyte. Can be used to “fine tune”.
3) The operation of the ASIC 40 to change the sampling characteristics such as the sampling rate, the integration and reset periods of the capacitor 67, and the resolution of the resulting signal. These affect the amount and quality of the output data. In general, increasing the sampling rate reduces the chance of missing an actual event, but increases the noise that causes poor measurement quality at each observed event, and vice versa.

In order to satisfy the local performance target, the controller 58 further controls the operation of the analyzer 13 as follows, for example.
4) A bias voltage source 59 that changes the bias voltage across each nanopore. This is an electrical parameter for biochemical analysis that affects performance.
5) Control the switch component 62 to change the nanopore to which an electrical signal is supplied to the detection channel 65.
6) Add more fluid to add more nanopores to the functional array of amphiphilic membrane 26 with no or some nanopores.
7) If the sensor device 14 is measuring insufficiently as a whole, add more samples.
8) Add another sample when the measurement requirements for one sample are met.
9) A reverse bias potential is applied to “unblock” the nanopore when no current flows through the individual nanopore.
10) If a global fault setting at the chip is reached, or if a new sample to be measured is needed before being introduced, or if another kind of nanopore is needed to measure the sample, Apply a bias potential sufficient to destroy all amphiphilic membranes 26 and then reset the analyzer 13 by re-preparing the analyzer 13.

  In the case of polynucleotide sequencing, the analyzer 13 may contain control DNA that is added into the actual sample. This allows quality monitoring of status at individual nanopores. The data obtained by the addition of the control sample can also be used to adjust and improve the algorithms used to process the data produced from the actual DNA sample, running in parallel.

  The control module 80 can also control signal processing functions, for example, to control a pipeline 74 that performs variable level data processing.

  The control module 80 performs the determination of performance measurements and the control of operation repeatedly, usually continuously, between biochemical devices. In this way, the operation of the single module 2 can be optimized in real time by using the module 2 more efficiently. When the control module 80 determines from the performance measurement that the biochemical analysis is complete, the control module 80 controls the controller 58 to end the biochemical analysis and also controls the loading device 54 to remove the cartridge 2. To do. Module 2 is then ready to insert a new cartridge 2, which is automated as part of the entire workflow pipeline for one or a series of experiments performed by the instrument to meet the user's global requirements. It may be performed by the following procedure.

  In the method described above, each module 2 is a stand-alone device that can perform biochemical analysis independently of the other modules 2. Next, it will be described how a cluster composed of modules 2 operates as a common device 1 in order to perform a common biochemical analysis. This is realized by a cluster of modules 2 that are connected to the network 3 via the network interface 53. In summary, module 2 connects to network 3 as a self-aware network device following the widely used “appliance” model. Thus, module 2 can implement data services and communication services. The configuration and protocol are stored and executed as part of the control module 80. Each module 2 can operate as a client for data and service, and can also operate as a server for data and service with respect to another optional module 2. Therefore, any number of modules 2 can be clustered together to become a large logical device 1.

  Module 2 also provides consistent data quality from each module 2, ie, filtering rules for output data, shared output locations, and non-conflicting data from the same name substrate to a shared storage location. It is also possible to communicate to share other information such as dynamically determined calibration criteria that allow simultaneous output.

  Each module 2 includes a web service module 82 that provides a graphical user interface (GUI) and an integrated / control application programming interface (API).

  The GUI is provided to the external computer 7 via the network 3 and displayed on the external computer 7. For example, the GUI may be provided in HTTP on a standard HTTP port that allows viewing in a conventional browser, or in any other format. The user can see the displayed GUI and can connect to this web service using a standard protocol (eg, HTTP) using a GUI to perform user input to module 2. The GUI may be a series of web pages that allow control of the module 2, input of parameters, display status, and display data etc. in a graph. The user can see the status of the selected module 2 and can send commands to that module 2 via this interface. This same service runs on all modules 2 and can be connected in the same way. The GUI may be replaced with any other suitable interface such as a command line.

  The API allows modules to interact with each other.

  The GUI allows the user to address modules 2 to select any number of modules 2 to operate as a cluster for performing common biochemical analyses. Since each module provides a GUI, any module 2 can be accessed by the user and used to select multiple modules 2. This causes the API to send a command to all modules 2 in the cluster and inform them that they are being addressed. The modules 2 selected for the cluster are temporary, called “namespaces” that identify those modules 2 mnemonically as a cluster for performing a common biochemical analysis to both the control module 80 and the user. Given any label.

  In addition, the GUI allows the user to provide input representing global performance goals for the device 1. Alternatively, the input representing the global performance goal may be derived by the instrument 1 and may be retrieved, for example, from a table storing global performance goals for different types of biochemical analyses.

  This global performance goal is of the same nature as the local performance goal, i.e., the upper limit on the amount of output data generated, the amount of output data generated, or Any combination of the quality of the output data to be output. Global performance objectives may be fully defined or some may remain undefined. For example, the requirement to generate a specific amount of data at a specific quality is defined by setting quality and quantity targets while leaving the time target unset. For example, a module's global performance goal is a given time period, eg, 6 hours, with a minimum required level of data quality, for example, a minimum average error rate of less than 1/1000 across all measured bases. In some cases, it is necessary to collect sufficient data to cover the sample more than 20 times (ie, oversample).

  The cartridge 10 is then prepared with an aliquot of the sample to be analyzed and loaded into the module 2 of the cluster. This step may be performed by the user. Alternatively, this step may be automated to some extent, for example, by a module 2 having sensors that allow automatic alignment of the cartridge 10. Next, a command is issued to instruct the module 2 of the cluster to start the analysis.

  In advanced systems, the preparation of the cartridge 10 with the sample to be analyzed and / or the loading of the cartridge 10 into the module 2 may be automated.

  In another alternative, the cartridge 10 is a mechanism for managing and processing a plurality of samples in a continuous or time-division multiplexed manner, for example, a well plate that continuously stores a plurality of samples processed by the sensor chip 14 100 using a mechanism having the structure shown in FIG. In this case, each module 2 controls the cartridge 10 loaded in each module 2 to process the sample from the selected well 102. The software of module 2 is set by the user to recognize which sample is in which well 102, for example by receiving user input. This adds one layer of information to sample management. All other operations in the cluster remain the same, and assuming that there is a sample mapping in each cartridge 2, all other operations in the cluster will cause which sample to move from a given well 102 of the plate 100. It is supported that the processing is also taken into account by cooperation. Therefore, this coordination is performed at the level of the sample for each plate 100 rather than the sample for each cartridge 2. When a new cartridge 2 is inserted, the control module 80 refers to the sample well table loaded by the user. This may also be accessed from a central database using an internal bar code provided on the cartridge 2 as a reference key (plate and sample information when the well plate 100 is attached to the cartridge 2 Associated with this cartridge by the user).

  The module 2 of the cluster “knows” that they are cooperating and their control module 80 communicates as follows to provide a control system for the entire device 1 together: And interact.

  FIG. 23 shows the control process.

  In step S1, based on the global performance target 90, a local performance target 91 for each module 2 in the device 1 that satisfies the global performance target 90 is obtained. Step S1 is a global decision that is performed for all modules 2 in the cluster. Initially, step S1 is performed based solely on the global performance target 90, but hereinafter, S1 is derived from each module 2 output data 92, as will be described below. Further execution is performed based on the performance measurement 93 in FIG.

  In step S2, a local control process for each module 2 in the cluster is executed, and this local control process is executed based on a local performance target 91 for that module 2. In FIG. 23, four such local control processes are shown by way of example, but typically there are the same number of local control processes as module 2. The local performance target 91 effectively indicates the operation required from each corresponding module 2, and in step S2, each module 2 operates according to the local performance target 91 in order to perform the necessary operation. Module 2 thereby performs a common biochemical analysis together.

  Step S2 itself includes the following processes.

  In step S3, based on the local performance target 91, the operation of the analyzer 13 is controlled by the method described above, ie, the start and end of the operation in the analyzer and / or the change of the operating parameters.

  First, step S3 is performed based only on the global performance target 90. However, when the operation starts, output data 92 is derived. As part of the local control process in step S2 for each module 2, in step S3, a performance measurement 93 is derived from the output data 94 as described above. Next, in the local control process of step S 2 for each module 2, step S 3 is executed based on the performance measurement value 93 and the local performance target 91. In this way, the control of the operation in each module 2 is changed based on the performance measurement value 91 actually realized by the module 2. The control performed in step S3 is repeated during the execution of the biochemical analysis in this manner by feedback of the performance measurement value 93 obtained from the output data 92, and is normally continuously updated.

  Furthermore, the performance measurements 93 from all modules 2 in the cluster are fed back to step S1 at least once during the execution of the biochemical analysis. Next, in step S1, the local performance target 91 is changed when necessary to meet the global performance target based on the performance measurement values 93 and the global performance target 90 from all the modules 2. Each module 2 then operates in step S3 according to the updated local performance target 91. The update of the local performance target 91 effectively indicates that the operation required for each corresponding module 2 has been changed. The operation of module 2 under the control of the control module 80 in accordance with the updated local performance target 91 changes to the required operation in module 2 to meet the global performance target 90.

  Such an update in step S1 for changing the local performance target is performed at least once as necessary, but is preferably performed repeatedly, preferably periodically, and preferably usually at least an order of magnitude. Much longer than the cycle of biochemical analysis, and usually at least one order of magnitude, at intervals much longer than the cycle in which the control of the operation of the module in step S3 is updated. Increasing the frequency of updates improves the management of module 2, which sacrifices the resources occupied by the embedded computer 51 and network 3, and this improvement is the interval that is unique to the event in biochemical analysis. Decreases as it approaches. Usually this interval may be on the order of 1 to 5 minutes, but the management of module 2 is still effective at longer intervals, for example intervals on the order of several hours. However, even a single update between biochemical benefits is an advantage over monolithic devices.

  In step S1, when setting or updating the local performance target 91 is attempted, the local performance target 91 of the module 2 necessary for satisfying the global performance target 90 cannot be achieved, so that the necessary operation may not be achieved. To address this, the control module 80 is configured to determine if this is the case and take remedial action. Various remedies are possible.

  One type of remedy is to increase the number of modules 2 in the cluster that are used to perform a common biochemical analysis. This allows the global performance target 90 to be met. To achieve this, the control module 80 can generate an output that notifies the user. In response, the user can use the GUI to address one or more additional modules 2 to form part of the cluster, introduce a sample into the cartridge 10, and one or more additional modules. Those additional modules 2 can be set up in the same way as the original module, including loading the cartridges into each of the two. Alternatively, these optional steps may be automated.

  Another type of remedy is to control the module 2 of the cluster to complete the biochemical analysis completely. This opens the module 2 for another biochemical analysis if the global performance target 90 may not be met.

  The determination in steps S1 and S3 may be execution of any suitable calculation method. The simplest approach is to use a contingency lookup table that is stored in the embedded computer 51 and executed in a given scenario. For example, one such scenario may be that a particular set of performance criteria cannot be met for one of the nodes under the execution node, and the remedy for that one is Can increase the data collection rate. Simple logic based on the program, coded in software, can be used to analyze the data and derive decisions. Other more complex methods may include fuzzy recognition in a specific pattern of data and generation of a response, such as by a neural network after learning.

  Next, the case where various steps in the control process shown in FIG. 23 are performed will be described.

  Step S2 is a local control process for each module 2 that is performed based on the local performance target 91 of that module 2 and requires the calculation of performance measurements 93 from the output data 92. Thus, advantageously, the control module 80 of each module 2 performs the local control process of step S2 on its own module. In this way, the control of the operation in step S3 and the determination of the performance measurement 93 can be performed locally in the module 2 without requiring any data to be transmitted over the network. This supports the scalability of the control process with the number of modules 2. Since each module 2 independently executes the local control process of step S2, any number of modules 2 can be used without increasing the burden of data transfer through the network 3 necessary to implement the local control process of step S2. Module 2 can be included in the cluster. This also effectively shares the processing load of step 2 between the modules 2 because each control module 80 performs processing of the control module 80 itself.

  Basically, step S3 or step S4 can be carried out externally to one or more modules 2, ie in a different module 2 or in a further computer connected to the network 3. In order to execute step S4 externally, it is necessary to transmit the output data from which the performance measurement value 93 is derived via the network 3. Similarly, in order to execute step S3 externally, it is necessary to transmit the performance measurement value 94 and the control signal of the module 2 via the network 3. This increases the burden on the network, especially if the control is frequently changed in step S3. For any realistic implementation in network 3 and external processing, this will create a bottleneck for data transfer and / or processing. Such a bottleneck will reduce scalability by effectively limiting the number of modules 2 that can be incorporated into the cluster.

  When step S1 is performed, the level of flexibility is increased. Step S1 requires a performance measurement 94 of all the modules 2 considered, so that some data transfer over the network 2 is required so that step S1 is performed based on the performance measurement 94. However, the amount of data required for transfer, which is the performance measurement value 94 and the message for performing exchange between the control modules 80, is relatively small. This requires a much smaller amount of data than the output data itself. For example, the performance measurement values only represent a handful of each measurement value, but the amount of output data that is array data is larger, and the amount of output data that is event data is usually more than array data. The amount of output data representing a measurement signal is one digit larger than that of event data. Furthermore, since the control in the operation of the module is updated in step 1 with a period that is much longer than the period in which it is updated in step S3, data is less frequently required to be transferred over the network 3, thereby further The load on the network 3 is much lighter than when step S2 is performed outside the module 2.

  In the first embodiment, the process of step S1 is shared among the control modules 80 of module 2 in the cluster. In this case, the control module 2 executes step S1 in cooperation with each other to obtain a local performance target 91 for each module 2 in the device 1 that satisfies the global performance target 90 together. This can be achieved by an iterative process. Each control module 80 derives its proposed local performance goal for itself and then communicates it to the other modules 2 in the cluster. Upon receiving the proposed local performance goal from all other modules 80, each control module 80 determines whether the global performance goal is met and, if necessary, the proposed local performance goal of the control module 80 itself. Modify performance targets. This process is repeated until local performance goals are accepted.

  When step S1 is first executed, this is done based only on the global performance target 90 because no output data has yet been generated. Next, if step S1 is performed to update the local performance target of each module 2 as needed, step S1 is derived for that module 2 by the control module 80 in each module 2. Based on the measured performance value 94. For this reason, the control module 80 transmits the performance measurement values 94 to each other via the network 3. In this way, the control module 80 actively reports the performance measurements 94 to each other in order to complete the biochemical analysis most efficiently. Each module 2 can reach a decision on the module 2 itself. The decision can then be encoded in a lookup table in the respective module 2. Each module 2 then sends its determination of the module 2 to the other module 2 via the web service, whereby each module 2 is then proposed a table of the other module 2. Remember as a response. Once more than one is signaled, a simple majority vote can be applied against this table to select the proposed course of action.

  In this way, the control modules 80 in each module 2 can perform the necessary calculations and decisions without user input, but these control modules 80 can also collectively perform the same thing. . These control modules 80 can also share individual internal decisions and, for overall results, collective decisions can be made collectively at a level that exceeds individual internal decisions. In this way, the integration / control API consolidates decisions across module 2 of the cluster to optimize the laboratory workflow.

  As described above, the modules 2 in the cluster constituting the device 1 generate output data of a plurality of channels from a common biochemical analysis. The module can optionally include an integration layer (not shown) to allow consistent filtering, normalization, and aggregation of its output data. In the case of polynucleotide sequencing, module 2 may be controlled to perform sequencing analysis on a single sample in concert and at high throughput, whereby each module 2 is flow cell based. This corresponds to a subchannel or “lane” of a DNA sequencing device in optical measurement.

  This first embodiment supports the scalability of the control process with the number of modules 2. Each module 2 contributes equally to step S1, thereby sharing the processing load equally, and the processing load in a single module 2 increases to a minimum by an increase in the number of modules 2 in the cluster. With the increase in the number of modules 2 in the cluster, the amount of data transmitted by the network simply increases in proportion to the number of modules 2. Basically, this results in a limited cluster size for a given realistic network 3, but the amount of data is relatively small, so in practice a large number of modules Can be imported.

  In this first embodiment, as each module 2 participates in the decision process, this shares the processing load, and since all of those modules 2 have the ability to make decisions, this is The device 1 has the advantage that it can be formed from any combination of modules 2. However, decisions are shared in different ways.

  In the second embodiment, in order to determine the local performance target 91 of all the modules 2 in the cluster based on the performance measurement value 94 transmitted from the other module 2, the process of step S1 is a module that acts as a master. Performed by two single control modules 80 or by a control module 80 in a subset of modules 2. This still requires data representing performance measurements transmitted over the network 3, increasing the processing burden on the module 2 acting as a master. Ideally, any module 2 has the ability to act as a master, whereby a master is arbitrarily selected from any module 2 addressed as a cluster. Alternatively, only the special module 2 can act as a master, but this has the disadvantage that it requires the user to select one of the modules 2 in every cluster that is addressed.

  In the third embodiment, the process of step S1 is performed by an external computer 7 that acts as an integrated control unit to make a decision on a local performance target, or a dummy module 2 that does not have an operable analyzer 13 or the like. By a further computer connected to the network 3. In this case, this further computer becomes part of the overall control system, and the performance measurements are communicated from module 2 to the further computer to form the basis for the decision. However, the requirement for a further computer that is suitably programmed is disadvantageous in that it means that the remote module 2 is insufficient to perform control. On the other hand, this embodiment reduces the processing requirements of the module 2 itself.

  Another alternative towards further nesting level feedback has been introduced into the control process shown in FIG. In FIG. 23, there is feedback of performance measurements 94 at two levels, the first being the level in the local control process of step S2 for a single module and the second being the cluster. It is a level in the whole. Further levels may be introduced by dividing module 2 of the cluster into logical groups of modules 2 that are respective subsets of the total number of modules 2 in the cluster. The performance targets and performance measurements for each logical group are obtained in the same manner as the local performance targets and performance measurements for the individual modules 2, as described above. Step S1 of the control process shown in FIG. 23 is modified to include an additional level of feedback. That is, at the highest level, group performance goals are determined based on the global performance goals and performance measurements for each group. At the next level, in a separate group control process for each group, the local performance goals for each module 2 in that group are determined based on the group performance goals and performance measurements for each module 2 in that group. Similarly, the performance measurement value for the entire group is obtained from the performance measurement value of each module 2 in the group. In general, any number of nested levels of feedback can be used, for example, by dividing a group into subgroups.

  In this case, as described above, any level of feedback can be provided using any embodiment for step S1.

  This alternative increases the complexity of the control process but has the advantage that the control process can be adapted to common biochemical analysis properties and / or various network structures. Different levels of control processes may be implemented in different elements in the device 1 and may be updated at different periods, with the inevitably reduced burden on the network 3. For example, a group may be a group of modules 2 that perform the same part of a common biochemical analysis that is advantageously controlled with reference to group performance goals for the entire group. Alternatively, the group may be a group of modules 2 connected to respective local networks interconnected, for example by the Internet, in which case the data flow between the local networks may be any arbitrary attached to the local network. Reduce without affecting control in individual groups.

  Next, how the module 2 connects to the network 3 and communicates on a peer-to-peer basis will be described. Generally speaking, because low update frequencies are acceptable, the exchange of state data between modules 2 that inherently facilitates automated decisions for performance management is based on “eventual consistency”. It is.

  Modules 2 can be distinguished from each other using a service discovery protocol such as Universal Plug and Play (UPnP) or Zeroconf (or Bonjour).

  Metadata such as proposed local performance goals and performance measurements can be conveyed using various types of distributed database technologies such as CoachDB (HTTP, JSON), Tokyo Cabinette, or MemcacheDB.

  Alternatively, discovery and metadata transmission can generally be achieved using messaging techniques such as network broadcast, network multicast, The Spread Talkkit, ActiveMQ, RabbitMQ, or message queues.

  One possible embodiment uses a single script that operates in publisher, subscriber, or public + submode to implement network broadcast of beacon packets using User Datagram Protocol (UDP). That is, each beacon packet contains encoded JSON (plain text Javascript object notation) data. Each module 2 acts as a node that broadcasts its own details and listens for others. The received beacon packet is decoded and incorporated into an internal in-memory data structure, such as one hashed with the module name. This has the advantage of simplicity, and the beacon packet contains a minimum amount of peer name (host name by default), peer time, and system performance & status data. Module 2 then retransmits the entire module 2 data structure, including the data received from other modules 2. This retransmission increases the likelihood that module 2 will receive data from other modules 2 because the UDP packet is unreliable and the transmission of the beacon packet is not guaranteed. Because the beacon packet can contain data for all modules 2 in the cluster, module 2 does not capture external data that is said to be from module 2.

  UDP packets are most efficient up to a maximum transmission unit (MTU) of the subset. By default, this is about 1500 bytes. Payload compression (eg, using common gzip / LZW) may be useful to keep the transfer size below the MTU. For a fixed beacon frequency, as the number of modules 2 in the cluster increases, the risk of network packet collisions and retransmissions causing congestion and loss of bandwidth is much greater. This can be addressed by using a dynamic beacon frequency that is inversely proportional to the number of active modules 2.

  The advantage of the device 1 is that an improvement in efficiency can be achieved compared to a monolithic device by modularizing the analyzer 13 itself and intelligently parallelizing the operations of the individual modules 2. The user has a parallel group of modules 2 at will and wants to create a cluster of any number of such modules to meet common biochemical analysis requirements that are desired to be performed. It can be grouped into large-scale devices 1. This scalability makes it possible to perform biochemical analyzes of varying complexity without being constrained by the capabilities of a single instrument. Similarly, by controlling the operation of module 2, the performance of module 2 is optimized to meet global goals. Both of these factors lead to efficiency gains because the desired utilization in the individual modules 2 is made, effectively freeing other modules 2 to perform other tasks.

  For example, a small number of modules 2, or even one module 2, can be used for low-throughput applications, and large clusters can be used, for example, large sequencing projects such as human genome sequencing. Can be used for large-scale parallel use. This allows for workflow management, which improves efficiency in device usage. In the sequencing example, the resulting workflow overcomes the challenges associated with current monolithic DNA sequencing instruments and meets the needs of users performing large-scale genome sequencing projects that require high throughput. On the one hand, it also accommodates the needs of intermediate laboratories performing relatively small but highly repetitive plans, or heterogeneous plans, or just small experiments.

• Re-sequencing / assembly of the human genome • Low-coverage methylation or cancer reconstitution • Highly repeated short readout experiments such as gene expression • Single molecule analysis using small samples or mixed cell populations In order to perform various types of analysis such as, the device 1 using various numbers of modules 2 can be applied.

Next, some specific examples regarding the situation where efficiency can be obtained will be described.
1) A user sets up a cluster of 10 modules 2 to measure DNA from a single sample. The user sets up the experiment so that 10 aliquots of sample are added to each module 2 to provide the required sample material and selects the user's preferred settings (eg, completion time, data quality, etc.) Then start the experiment. One module 2 has a defective chip and reports almost no data. Since the user requested the end of the experiment at a specific time, the other nine modules 2 in the cluster were able to meet their goals by automatically manipulating the temperature to increase the processing speed of each nanopore. Increase the sequencing speed of 2. Without this dynamic readjustment, the experiment will end within the set time frame, but the experiment will generate less data than the user expected, and the user's performance and overall experiment The result can be compromised.
2) In another example, a user creates a cluster of 8 modules 2 to measure a single sample, which is again equally divided over 8 modules. Four of the eight modules 2 report very low data quality and the other four are pre-specified performance parameters required by the user (eg measurement output and quality) Can not compensate for. Thus, the faulty module 2 terminates their operation and sends an email to the operator to report what was done and why, so that the operator has the least time loss or cost to the user. Allows another aliquot of the sample to refresh the nanopore on the same chip in the defective module 2, or allows another set of four chips to be loaded immediately, thereby Any time loss is minimized. In this example, defects can be detected early in the run, and additional chips can be loaded before there is no time to complete for the sample, thereby relieving the project. become. In comparison, if the user is doing the same experiment with Illumina's Genome Analyzer, four of the eight “lanes” will have defects that cause low-quality data generation. Simply terminate all experiments early and either lose all the data generated across all lanes up to that point, or allow the completion of the drive, and eventually nearly half of the expected amount Although it only has high quality data, it will cost the same cost and time as a fully functional experiment.
3) As a continuation of the above scenario, another useful situation can occur. The user's laboratory has only eight installed modules 2 and the four failed are open. However, another emergency project is waiting for operation in the system. The operator can then make a decision to allow more time for the completion of the original project in the remaining modules 2 and use the four open modules 2 to handle the waiting project as conveniently as possible. In this way, resources can adapt globally to laboratory priorities.
4) The user wants to perform an experiment on a sample, or an array of samples, to examine special results on those samples. Thus, the user can specify that specific data (eg, an exact DNA sequence motif) is observed once or that an experimental process in one or more samples continues up to a specific number of times. . In particular, once all data sets have been analyzed, the data can be used as a marker or proxy for the overall potential success in the experiment. For example, a certain level of coverage in a specific region of the genome uses the same library of DNA fragments to ensure total coverage across the sample (the degree of oversampling) sufficient for the investigation required by the user. From the previous sequencing operation. In a cluster of modules 2, such a survey may be shared across modules 2, and once sufficient data of the required type is observed, this is for some or all of the modules 2 that are participating Can be used to set end conditions. This optimization of time and cost to experimental results cannot be performed with current DNA sequencing instruments.
5) The user sets the requirements for the cluster consisting of modules 2 to analyze the DNA sample with high quality specified in advance. During the experiment, Module 2 collects more data than the user expected, but it is not of high enough quality. In order to quickly reach the required quality goal, module 2 collects the analysis conditions of module 2 to improve data quality at the expense of throughput (a given amount of data has already been achieved) Adjust to. For example, by lowering the operating temperature, DNA bases pass through each nanopore more slowly on average, thereby allowing more analysis time per base and thereby data output per nanopore But the quality of base measurement is improved. Alternatively, or in parallel, the pace at which the current flowing through each nanopore is measured may be altered to result in fast or slow sampling, thereby causing signal-to-noise characteristics and passing through the nanopore Depending on the speed of the base, certain aspects of data quality can be improved.
6) During the experiment, one of the modules 2 in the cluster encounters a sudden hardware failure and shuts down safely, causing experimental data loss (but generated by module 2 by the time of failure) All the data that has been sent is available and has already been sent to a dedicated storage area). All the remaining modules 2 respond by increasing their scheduled experimental timeframes to meet the preset user needs in the required data output without user intervention. In addition, the system sends an automatic message to the manufacturer to order a replacement item. Minimal disruption to user experiments and workflows.

For example, if the cartridge 2 is capable of processing multiple samples as for the structure of FIG. 11, examples of global performance goals that can be met are as follows.
1) A sample is being processed on the plate 100 at a node of a cooperating cluster. The user has specified that a certain amount of data is required. This sample is on a separate plate 100 and is being processed at another cluster node. Module 2 cooperates as described above.
2) The scenario shown in 1 is used, but in this case the second sample on the second plate is of poor quality. Module 2 examines the internally stored plate-sample table to see if another instance of the sample is on the plate 100 of module 2, and if there is an instance, resets its valve and degrades By using this sample rather than the prepared sample, the performance target is met and cooperation continues.
3) In another example, ten modules 2 are processing plates 100 of the same sample and are operating through those modules 2. The user changes the priority of one of his samples that has not yet been processed. Next, some of the modules 2 in the cluster reset their valves and move to the sample to deliver the sample data on time. The remaining modules 2 in the cluster continue to process the original sample and increase the processing speed by changing the temperature.
4) In another example, the cluster of modules 2 is processing the same plate. Before module 2 begins, module 2 sets valve 110 and proceeds to well 102 where it takes a short sample and runs for a short time. Thereby, both modules 2 pre-calculate the probable data quality and amount of data that originates from each sample (or well 102). Both modules 2 then calculate the optimal order for processing the samples to deliver the required quality and quantity data to each user according to a preset priority. If the well 102 is found to be empty, or if the sample is too low quality to meet the goal, the cluster will need to replace the useless sample and prepare an unused plate. Notify

  The key to success is the ability of module 2 to determine sufficient, sometimes pre-set termination conditions, individually and cooperatively. This ensures that neither data that is too low in required quality nor data that is excessive is generated. In this way, full utilization in the system is achieved, and no “slack” data is generated in excess. Overall, no extra operation needs to be done after the fact to adjust for any deficiencies in output or quality. This overall method allows samples and data to be efficiently pipelined throughout a sequencing workflow that optimizes throughput, quality, and cost. For any high-end laboratory, this is a system that operates for a fixed number of operations with a fixed data output, especially if the output data is not always predictable, as is usually the case. Several times improvement in efficiency can be achieved.

  It should be noted that all the above operations are made possible and performed by the implementation of specific controls shared within the respective modules 2. Furthermore, it should be noted that the module 2 can be operated independently and more, but not all of the above scenarios can be implemented with a single module 2. Although internal optimization can be performed, optimization across several modules 2 cannot be performed.

  Next, the operation of the device in Example (1) will be described in more detail.

  In this case, the device 1 is used for DNA sequencing. This means that at least four possible analytes corresponding to the bases G, C, A and T are detected. Ten modules 2 are used and they are given the same sample for processing. Users need 12 gigabase (109) of data per day, of which 100% of the recorded bases have a quality score of Q20 or higher (ie less than 1 out of 100 bases are wrong) Have a certain probability). The amount and quality of the data is chosen so that it is almost certain that the user will be able to find the genetic factor (eg, the mutant the user is looking for) when the DNA sample is analyzed. These criteria may be derived from previous empirical or some situation.

  The user has at least 10 modules 2 in suitable locations and knows the network address of the embedded computer 51 in each module 2. The user prepares the module 2 DNA sample in a manner appropriate for a given experiment. If this is human genome sequencing, the user can randomly cut DNA samples using suitable off-the-shelf equipment.

  The user has decided to use 10 modules 2 for this sample based on the expected throughput (data per unit time). The sample is introduced into ten cartridges 10 loaded in the module 2. The module 2 can automatically read the barcode or RFID of each cartridge 10 that uniquely identifies the cartridge 10 and store the ID of the cartridge 10.

  Module 2 identifies another module 2 in the cluster, sends a handshake, and receives basic information about the other module 2. This information is then displayed by the GUI. In this example, the user can see 20 modules 2 in this network, but the user is only interested in 10 with the cartridge 10 loaded with the user's sample. These are identified via the GUI by name, address, status, location, etc., all of which are verified from the underlying web service. In this way, any module 2 can be used to manage any other module 2, and no other computer is required. Accordingly, any number of modules 2 can be connected, managed, and operated in a manner that can be linearly scaled without having a bottleneck in operation through the gateway system.

  Next, the user addresses the ten modules 2 as targets through the GUI. The GUI element allows an assigned name (eg, “Human”). The same GUI allows commands addressed to this collection only, any data returned from these modules 2 that will be treated as an aggregate, and separately returned from any other cluster of modules 2 Arbitrary data is allowed. The user can also directly focus on other information about the sample under experiment, or the entire process can be linked to an external database system.

  The user then tells the “Human” cluster consisting of module 2 that the module 2 will be operated through the GUI until the 12 gigabase Q20 + DNA sequence data is collected. Module 2 is also told that module 2 operates on the same sample. The control module 80 in each module 2 executes these commands and stores performance measurements such as how much data is collected and how the quality is. Other metrics may be useful for various use cases. This control module 80 can monitor and determine the data and status of module 2 in real time or near real time. In this case, the control module 80 stores that the control module 80 belongs to a group called “Human” and that the entire group has a goal of cooperation in Q20 data of 12 Gb. This may simply be stored internally as a table in memory in this process indicating the name of module 2, generated data, target data, quality, etc. or as in Table 1, for example In addition, it may be stored in a more permanent storage device.

  As shown in Table 1, each module 2 in the group “Human” shares this table (data structure). A standard part of the operation of module 2 will be broadcast via web service 82 inside module 2 and a copy of this table will be sent to other modules 2 at regular intervals, so that other Synchronize module 2. Each module 2 can then examine the status of the other modules 2 and perform pre-scheduled operations at any time, such as summing the “Output” column and comparing the totals to the “Group Target” column. Data generation at a given quality for any individual module 2 or run time sequence indicating whether the sum of the outputs of module 2 is heading to meet the time requirements set by the user The speed can be inserted by another calculation. Each module 2 has these calculated values encoded in its control module 80, and each module 2 is in a shared, synchronized status data table in module 2. Based on the periodic calculation. A number of such calculated values are encoded in the control module 80, covering other use cases other than this simple example. After 6 hours, it can be seen that the amount of data generated is not on track to meet the target, and each module 2 will notice this internally. One of the added modules 2 seems to be working poorly. This may be due to any number of reasons, but the on-board diagnostic information does not indicate any failure.

  Next, module 2 makes a decision based on the information that module 2 has in order to meet the goals of module 2 as described above. In this case, the course of operation chosen from all the modules 2 is to increase the output of the functioning module 2. This table was a match. Summarizing the results internally, module 2 must then calculate how much additional data is needed to achieve the goal. These modules 2 already know internally how much data each of these modules 2 generates per unit time and how much other modules 2 are generating from other modules 2 Have also gained. Using pre-coded logic associated with the selected course of operation (ie, software function), module 2 then calculates the amount of module 2's own output needed to increase to meet the goal. To do. In the simplest algorithm, each module 2 proposes a small increment in a proportion and sends it to the other modules 2. Next, each module 2 uses its internal table to calculate what effect this has on the basis of tabulations and target outcomes. This process is repeated until all of the modules 2 indicate by their internal tables that the goal has been reached. In a more advanced alternative, the low power module 2 makes the proposed increase over the module 2 with good power, and thus “load sharing”. Again, the same data sharing, then shared computation, then result sharing, and then community voting are used to allow module 2 to select a collective action course. To do.

  In this example, as shown in Table 2, some modules 2 (only three are shown) are able to compensate for weakened modules 2 from 1 Gb per day to 1.4 Gb per day. In addition, the internal table is updated to increase the local performance target of module 2. Calculations show that if there are no other changes, the total output of the entire group will meet time and quality goals. Thus, the module 2 adjusts the internal logic of the module 2 by feedback from the other modules 2 in order to meet the collective goal.

  Once this is done, the individual modules 2 then need to move the collective decision to an internal remedy. The logic for doing this is encoded in the control module 80. For example, the temperature of the sequencer can be used to control the rate at which nucleotides are cleaved from the DNA strand and passed through the nanopore. This can cause a slight degradation in the quality of the observed data if the temperature rises too high (see below), but this is detected by the basic procedure described in the steps above and the degradation in quality. Try to correct. In this case, the remedy is high throughput in the base. Thus, the control module 80 sends a command as a suitable function call or an RPC call by sending a formatted string to the communication socket to the microcontroller 58 of the internal board 50. This command instructs the microcontroller 58 to change the temperature of the analyzer 13. This may be implemented by sending further commands to the device driver that is controlling the temperature control element 42. The “set” temperature for this part is probably an increase derived from the look-up table, and is increased by the expected increase to increase the number of bases per unit time by the desired amount. The temperature control element 42 responds by cooling lower, and sensors on the board of the cartridge 10 sense the temperature change relative to the desired level. This information, recorded value, arbitrary error code, etc. are then returned to the control module 80 which records it, and the remedy is taken safely.

  The control module 80 always records and counts bases and quality scores from the data when the data is transferred from the ASIC 40 and processed by the processing module 73. This process continues, the internal table is updated, and the results are sent to the other modules 2 in the group. Everything is good and the device 1 as a whole is on track to achieve global performance goals. Then, if no further action needs to be taken, other scenarios are explored. These scenarios will follow the same basic data flow but have specific logic encoded in the software module that is accessible by the control module 80. For example, if the measure here fails to meet the time and quality requirements after adjusting the temperature, module 2 sends a message to the user (recorded at run time) to send multiple messages to meet the goal. An indication that the additional module 2 is required may be determined. This means that the user reassigns work to other, possibly unused modules 2 and inserts additional cartridges 10 with the same sample in the manner described above, and those modules 2 participate in collective operation. Allows those modules 2 to be added to the cluster.

  The core method is to allow collective decisions across modules 2. Modules 2 each have the ability to operate independently, but it is also possible to share internal data structures about status and keep them updated. Once these modules 2 are integrated and connected to a cluster that is a collaborative system, they execute a stored protocol that corresponds to this structure and / or a stored protocol that modifies this structure. Can do. This protocol not only allows communication between the modules 2, but also allows the module 2 to change the operation of the module 2 and coordinate the change with other modules 2. Begin execution of pre-coded logic running on the computer.

  Module 2 cooperates to perform a biochemical analysis common to module 2 of instrument 1. Each biochemical analysis performed in each module 2 may be the same or different and, roughly speaking, means that global performance criteria can be set for all analyses. Only need to be “common”. Typical examples are different aliquots that are the same sample, or different, but possibly related to some method, eg, the same analysis performed on a sample sampled from a given population. This is for biochemical analysis performed in Module 2. Another typical example is the different aliquots that are the same sample, or the raw performed in each module 2 to be a different but related type of analysis performed on a different but possibly related sample. For chemical analysis.

  Further details relating to the nature of the biochemical analysis that can be performed are as follows. Subsequent paragraphs refer to documents that are all incorporated by reference.

  The analyzer 13 can perform biochemical analysis using nanopores in the form of protein pores supported by the amphiphilic membrane 26.

  The properties of the amphiphilic film 26 are as follows. In the case of amphiphilic systems, membrane 26 is usually composed of lipid molecules, or analogs of lipid molecules, either naturally occurring (eg, phosphatidylcholine) or synthetic (eg, DPhPC, Diphytanoylphosphatidylcholine). Non-natural lipid analogs such as 1,2-dioleoyl-3-trimethylammonium-propane (DOTAP) may be used. The amphiphilic membrane may be composed of a single species or a mixed species. Additives such as fatty acids, fatty alcohols, cholesterol (or similar derivatives) may be used to regulate membrane movement. Amphiphilic membranes provide a high resistance barrier to ion flow across the membrane. Further details of amphiphilic membranes applicable to the present invention are provided in WO-2008 / 102121, WO-2008 / 102120, and WO-2009 / 077734.

  In the analyzer 13, the amphiphilic film 26 is formed across the well 22, but the analyzer 13 can be configured to support the amphiphilic film by other methods including: The formation of an electrically addressable amphiphilic membrane can be achieved by a number of known techniques. These can be divided into membranes or bilayers that are built on one or more electrodes and provide a divider between two or more electrodes. The membrane attached to the electrode can be a bilayer or monolayer of amphiphilic species, and direct amperometry or impedance analysis can be used, examples of which are described in Kohli et al. Biomacromolecules. 2006; 7 (12): 3327-35, Andersson et al. Langmuir. 2007; 23 (6): 2924-7 and WO-1997 / 020203. The membrane that divides two or more electrodes is not limited, but can be folded (eg, Montal et al., Proc Natl Acad Sci USA 1972, 69 (12), 3561-3666), impregnated at the tip (eg, Coronado et al., Biophys. J. 1983, 43, 231-236), liquid suitable (Holden et al., J Am Chem Soc. 2007; 129 (27): 8650-5, and Heron et al, Mol. Biosys. 2008; 4 (12): 1191-208), glass support (eg, WO-2008 / 042018), gel support (eg, WO-2008 / 102120), gel encapsulation (eg, WO 2007/127327). As well as It can be formed in a number of ways, including a flowed porous support (eg, Schmitt et al., Biophys J. 2006; 91 (6): 2163-71).

  Nanopores are formed by protein pores or channels introduced into the amphiphilic membrane 26. The protein pore or channel may be a natural or synthetic protein, examples of which include WO-00 / 79257, WO-00 / 78668, US Pat. No. 5,368,712, WO-1997 / 20203, and Holden et al. Nat Chem Biol. 2 (6): 314-8. Natural pores and channels are those where the protein membrane is attached to alpha-hemolysin (eg, Song et al., Science. 1996; 274 (5294): 1859-66), OmpG (eg, Chen et al. , Proc Natl Acad Sci US A. 2008; 105 (17): 6272-7), OmpF (eg, Schmitt et al., Biophys J. 2006; 91 (6): 1633-71), or MsPA ( For example, it may include a structure including a beta barrel such as Butler et al., Proc Natl Acad Sci USA 2009; 105 (52): 20647-52). Alternatively, the portion of the protein membrane is covered with a potassium channel (for example, Holden et al., Nat Chem Biol .; 2 (6): 314-8), (Syeda et al., J Am Chem Soc. 2008). 130 (46): 15543-8), or the like. The pore or channel may be a naturally occurring protein that has been chemically or genetically modified to provide the desired nanopore behavior. Examples of chemically modified protein pores are provided in WO-01 / 59453, and examples of genetically modified protein pores are provided in WO-99 / 05167. Adapters can also be added to the system to allow further control and more targeted analyte detection, examples of which are described in US Pat. No. 6,426,231, US Pat. No. 6,927, No. 070 and WO200904170.

  Nanopores allow the flow of ions that move across the amphiphilic membrane 26. The ion flow is modulated by the pore based on the interaction with the analyte, thereby enabling the nanopore to perform biochemical analysis. For example, US Pat. No. 6,426,231, US Pat. No. 6,927,070, US Pat. No. 6,426,231, US Pat. No. 6,927,070, WO-99 / 05167, WO- 03/095669, WO-2007 / 056768, WO199070203, Clarke et al. Nat Nanotechnol. 2009; 4 (4): 265-270, and Staudart et al. Proc Natl Acad Sci US 2009; 106 (19): 7702-7707, there are many examples of such modulation that are used as the basis for biochemical analysis.

  The analyzer 13 includes DNA and RNA, and can use nanopores for sequencing polynucleotides including naturally occurring and synthetic polynucleotides. This analyzer 13 derives sequence information in a rapid and cost effective manner that generally utilizes measurements of changes in electrical signals at both ends of a single nanopore as a single strand of DNA passes through the nanopore. Therefore, various techniques proposed for this purpose can be applied. Such techniques include, but are not limited to, nanopore-assisted sequencing by hybridization, strand sequencing, and exonuclease-nanopore sequencing (eg, D. Branton et al, Nature Biotechnology 26 (10), p1- 8 (2009)). This technique can be performed by polynucleotide passing through the nanopore as an inert polymer (modified or unmodified), or (eg, US Pat. No. 5,795,782, EP-1,956,367, US Pat. 015,714, US Pat. No. 7,189,503, US Pat. No. 6,627,067, EP-1,192,453, WO-89 / 03432, US Pat. No. 4,962,037, WO 2007/056768, international application No. PCT / GB09 / 001690 (corresponding to UK patent application No. 0812693.0 and US patent application No. 61/076687) and (UK patent application No. 08126977.1) And the technology disclosed in International Application No. PCT / GB09 / 001679 (corresponding to US Patent Application No. 61/076695). Te) is cut to the nucleotide components or bases the may require polynucleotide.

  In general, the present invention can be applied to any device that realizes measurement in a nanopore by providing two electrodes on both sides of an insulating film in which the nanopore is inserted. When immersed in an ionic solution, the bias potential between the electrodes will drive the flow of ions through the nanopore, which can be measured as an electric current by an external electrical circuit. As DNA passes through the nanopore, this current changes with sufficient resolution, see, for example, Clarke et al. Nat Nanotechnol. 2009; 4 (4): 265-270, International Patent Application No. PCT / GB09 / 001690 (corresponding to British Patent Application No. 0812693.0 and US Patent Application No. 61/076687), and D.C. Staudart, PNAS doi 10.1073 / pnas. The constituent bases can be distinguished from these changes, as disclosed in 0901054106, April 2009.

  Furthermore, the present invention provides for an array of nanopores to measure the same sample by individually providing an addressable electrode on one side of each nanopore in the array, with each nanopore in the sample on the other side. It can be applied to any device connected to a common electrode or an equivalent number of addressable electrodes. The external circuit can then measure the DNA passing through each and every nanopore in the array without synchronizing the addition of bases to each nanopore in the array, in other words, each Nanopores freely process DNA single strands independently of each other as disclosed, for example, in US-2009 / 0167288, WO-2009 / 077734, and US patent application 61 / 170,729. be able to. Each nanopore is free to start processing the next strand once it has processed one strand.

  One advantage in nanopore-based analysis is that the quality of the measurement does not change over time with a fully functioning nanopore, in other words, the accuracy of base identification is determined at the start of the sequencing at a given point in the experiment. It is the same as the accuracy at any time after the restriction. This allows each sensor to perform multiple analyzes on the same sample or multiple samples sequentially over time with a constant average quality.

  In addition to polynucleotide sequencing, nanopores include, but are not limited to, diagnostics (eg, Howoka et al., Nat Biotechnol. 2001; 19 (7): 636-9), protein detection (eg, Cheley et al.,). Chembiochem.2006; 7 (12): 1923-7, and Shim et al., J Phys Chem B.2008; 112 (28): 8354-60), detection of drug molecules (eg, Kang et al., J Am Chem Soc. 2006; 128 (33): 10684-5), ion channel screening (eg, Seyda et al., J Am Chem Soc. 2008 Nov 19; 130 (46): 15543-8. , Defense (eg, Wu et al., J Am Chem Soc. 2008; 130 (21): 6813-9, and Guan et al., Chembiochem. 2005; 6 (10): 1875-81), and polymers (For example, Gu et al., Biophys. J. 2000; 79, 1967-1975, Mobilane et al., Biophys. J. 2005; 89, 1030-1045, and Maglia et al., Proc Natl Acad Sci U A. USA 2008; 105, 19720-19725) can be applied to a wide range of other biochemical analyses.

  Furthermore, the present invention can also be applied to an analyzer in which nanopores are provided in a solid film. In this case, nanopores are physical pores in a membrane formed from a solid material. Such membranes have many advantages over fluid or semi-fluid layers, especially with respect to stability and size. The original concept was proposed by researchers at Harvard University to test polymers such as DNA (eg, WO-00 / 79257, and WO 00/78668). Since then, this study has been based on the following techniques that can be applied to the present invention: manufacturing methods (eg, WO-03 / 003446, US Pat. No. 7,258,838, WO-2005 / 000732, WO-2004 / 077503, WO-2005 / 035437, WO-2005 / 061373), data collection and evaluation (eg, WO-01 / 59684, WO-03 / 000920, WO-2005 / 017025, and WO-2009 / 045472), nanotubes (For example, WO-2005 / 000739, WO-2005 / 124888, WO-2007 / 084163), addition of molecular motor (for example, WO-2006 / 028508), field effect transistor embedded in nanopore structure Or the use of analogues (eg US Pat. No. 6,413,792, US Pat. No. 7,001,792), detection of fluorescent probes interacting with nanopores or nanochannels (eg, US Pat. No. 6,355,420, WO-98 / 35012) and extended to include illumination and detection of the fluorescent probe (eg, US-2009-0029477) that is removed from the target substrate of the fluorescent probe as the fluorescent probe repositions the nanopore. The analyzer can even use mass spectrometry, for example, as the polymer of interest passes through the nanopore or channel, monomers in the polymer are cleaved, ionized, and then analyzed using mass spectrometry.

  Furthermore, the present invention provides a step-by-step approach, eg, prior to the imaging step to detect the incorporation, annealing, or removal of a chemically labeled fluorescent probe that allows for the decoding of the polynucleotide under experiment. Techniques other than nanopores, using periodic chemical reactions, measuring DNA polymerase activity in zero-mode waveguides (see, for example, Levene et al., “Zero-Mode Waveguides for Single-Molecular Analysis at High Concentrations”, 99 Science : 682-686, Eid et al., “Real-Time DNA Sequencing from Single Polymerase Modules”, Science. 323: 133-138, US Pat. No. 7,170,050, US Pat. No. 7,476,503), a technique for measuring the activity of a DNA processing enzyme in real time, a polymerase, and an incorporated DNA The presence of activated quantum dots, such as the activation of energy emission caused by fluorescence emission transfer (eg, US Pat. No. 7,329,492) between suitable chemical groups provided on both bases Below, a technique for measuring using activated quantum dots attached to a polymerase that acts on the DNA base incorporated into a newly synthesized strand containing a fluorescent group is excited, or new Measure local changes in ions (eg pH) to infer chemical activity as DNA bases are incorporated into the strand The present invention can also be applied to an analyzer configured to perform sequencing of a polynucleotide using a technique using an ion sensitive FET (for example, WO-2008 / 076406).

The present invention can also be applied to an analyzer configured to perform other types of biochemical analysis that does not use nanopores. Some examples of this analyzer are as follows. Further, the present invention is not limited,
1) ion channel screening,
2) Real-time DNA amplification (PCR, RCA, NASBA),
3)
a. Glucose oxidase b. G-binding protein receptor c. Enzyme activity by measuring changes in reactants or products, including gene activation of fluorescent proteins,
4) A reaction in which surface plasmon resonance is monitored, including dynamic binding of a ligand to a target molecule (eg, a protein to a chemical inhibitor)
5) DNA microarray for transcriptome analysis or infectious disease identification;
6) an antibody array chip for measuring proteins in a sample or solution, or
7) Perform other types of biochemical analysis without nanopores, including protein binding array chips that monitor the kinetics of protein interactions with substrates, targets, ligands, etc. by fluorescence or electromagnetic readout It can also be applied to configured analytical instruments.

  In each case, various experimental parameters can be modified to meet the user's global requirements for the experiment, including temperature, experiment time, readout sampling rate, light intensity or potential level, pH or ionic strength It is.

  The analysis may be a chemical or biological assessment and can be used to perform biomarker validation studies, clinical trials, and high throughput screening. These tests include chromatography (HPLC (High Performance Liquid Chromatography), TLC (Thin Layer Chromatography), along with detection of the analyte in the eluent (by absorption, fluorescence, radiometry, light scattering, particle analysis, mass spectrometry). ), FPLC (fast protein liquid chromatography), flash chromatography), or immunoassay, or direct mass spectrometry (MALDI (matrix-assisted laser desorption / ionization), APCI (atmospheric pressure chemical ionization)) , Use of ESI (electrospray ionization) ionization, time-of-flight, ion trap detection) by quadrupole (s). Immunological assays include ELISA (enzyme immunoassay), lateral flow test, radioimmunoassay, magnetic immunoassay, or immunofluorescence analysis.

  These tests and analyzes include Down's syndrome, genome-wide association studies, pharmacokinetic and pharmacodynamic studies on all tissues and animals, sports drug testing, testing for microorganisms in environmental matrices (drainage, contaminated water, etc.), processing It can be used in the case of tests for hormones and growth factors in water.

  The analysis can be applied to biomarker validation studies. The present invention allows a very large number of samples to be analyzed quickly and easily. For example, the current process in biomarker detection is hampered by a confirmation step, in other words, once a candidate marker is found, many samples are used to statistically confirm the level of change in the tissue of interest. Needs to be tested. Therefore, it is necessary to develop an analysis for each marker. The system of the present invention provides a single readout for all analytes such as DNA, RNA, protein, or small molecules, reducing the stage of developing the analysis.

  The analysis can be applied to clinical trials and surrogate for ELISA. When samples are submitted for testing in a hospital or clinic, it is very likely that the testing procedure will require mass spectrometry or ELISA. Both of these can be replaced by the system of the present invention. The development of a suitable test based on the system of the present invention will result in a large increase in throughput and savings in sample preparation time and handling. This would apply to large molecules such as growth factors, peptides such as insulin, or small molecules such as drugs of abuse or prescription drugs.

  The analysis can be applied to high throughput screening. Any quantitative screening can be performed by the system of the present invention. Thus, if analysis that results in peptides or small molecules (eg, analysis of proteases) is currently used for high throughput screening, the present invention increases throughput and reduces sample processing and preparation times. be able to.

Claims (46)

  1. A module for performing biochemical analysis, comprising an electronic circuit part and a cartridge that can be removably attached to the electronic circuit part,
    The cartridge is
    A sensor device capable of supporting a plurality of nanopores, operable to perform biochemical analysis of a sample using the nanopores, and comprising electrode components at both ends of each nanopore;
    At least one container for containing samples;
    At least one reservoir for holding material for performing the biochemical analysis;
    A fluid system configured to controllably supply a sample from the at least one container and material from the at least one reservoir to the sensor device;
    When the cartridge is attached to the electronic circuit unit, the electronic circuit unit includes a drive circuit and a signal processing circuit configured to be connected to the electrode configuration unit at both ends of each nanopore, and the drive circuit Is configured to generate a drive signal for performing the biochemical analysis, and the signal processing circuit uses the biochemical analysis from electrical signals generated from the electrode components at both ends of each nanopore. A module configured to generate output data representing the result of the.
  2. An analytical instrument for performing biochemical analysis, comprising a plurality of modules,
    Each module comprises an analyzer operable to perform a biochemical analysis of a sample, wherein the module is configured to generate output data in at least one channel that represents the result of the biochemical analysis. And the operation of the module is controllable to change the performance of the module;
    The analytical instrument is configured to receive an input that selects any number of modules as a cluster to perform a common biochemical analysis, and to receive an input that represents a global performance goal for the common biochemical analysis. Further comprising a configured control system, wherein the control system is configured to control operation of modules of the cluster to perform the common biochemical analysis;
    The control system is configured to determine performance measurements in each module from output data generated by the module at least once during the execution of the common biochemical analysis, the control system comprising: ) Based on the performance measurements determined for all modules and the global performance target, changing the control in the operation of the modules of the cluster and / or (b) the performance measurements determined for all modules An analytical instrument configured to take remedial action according to the global performance target that is not achievable based on the value.
  3.   The system of claim 2, wherein the modules are connectable to a data network and allow connection to each other via the data network.
  4.   The system of claim 3, wherein the control system comprises a controller in each module, the controller being operable to control operation of the module.
  5.   Providing the input to select any number of modules as a cluster, and enabling the user to address the controller to provide the input representing a global performance goal, the data The system according to claim 4, wherein the control unit is configured to provide to a computer connected to a network via the data network.
  6.   The control system is configured to determine a local performance target for each module based on the global performance target, and the controller in each module operates the module based on the local performance target of the module. The system according to claim 4 or 5, wherein the system is configured to control.
  7.   7. Each controller is configured to change control in operation of the module based on performance measurements determined for the module to meet the module's local performance goals. The system described in.
  8.   8. The system of claim 7, wherein each controller in the cluster module is configured to determine the performance measurement for each module from output data generated by the module.
  9.   The control system is configured to change the local performance target based on the determined performance measurement and the global performance target to change control in operation of the modules of the cluster. Item 9. The system according to any one of Items 6 to 8.
  10.   Each control unit in the module of the cluster is configured to obtain the performance measurement value for each module from the output data generated by each module, and transmit the performance measurement value via the data network. The system according to claim 9.
  11.   The controller in at least one of the modules of the cluster is configured to change the local performance target based on the determined performance measurement communicated over the data network. 10. The system according to 10.
  12.   12. The controller in all modules of the cluster is configured to cooperate to change the local performance goal based on the determined performance measurement communicated over the data network. The system described in.
  13.   The system of claim 12, wherein the controllers in the modules of the cluster are configured to cooperate on a peer-to-peer basis.
  14.   The controller in each module of the cluster determines the local performance target of the respective module based on the performance measurements of all modules communicated over the data network from other controllers in the cluster module. 14. A system according to claim 12 or 13, wherein the system is configured to change.
  15.   A controller in one or a subset of the modules changes the local performance target for all modules in the cluster based on the performance measurements for all modules communicated over the data network from the controllers of other modules 12. The configuration of claim 11, wherein the one or the subset of the controllers is configured to communicate changed local performance goals to controllers in other modules of the cluster. System.
  16. The control system further comprises an integrated control unit connected to the network,
    The integrated control unit changes the local performance target of all the modules in the cluster based on the performance measurement values of all the modules transmitted from the control unit of the module via the data network, and the changed local Configured to communicate a performance target to each control unit, wherein each control unit is configured to change control in operation of the modules of the cluster in accordance with the changed local performance target. The system according to claim 11.
  17. The performance measurements and global performance targets are
    Upper time limit for generating output data,
    The amount of output data generated, or
    The system according to any one of the preceding claims, representing at least one of the quality of the output data to be generated.
  18.   18. The control system according to any one of claims 1 to 17, wherein the control system is configured to repeatedly determine a performance measure for each module from the output data generated by the module during the common biochemical analysis. The system according to one item.
  19.   The control system (a) changes control in the operation of the modules of the cluster based on the performance measurements determined for all modules and the global performance target, and (b) is determined for all modules. 19. A system according to any one of the preceding claims, configured to take remedial action according to the global performance objectives that are not achievable based on measured performance measurements.
  20.   20. The system of claim 19, wherein the remedy is to increase the number of modules that perform the common biochemical analysis.
  21.   21. The system of claim 20, wherein the remedy includes generating an output to indicate to a user that additional modules are needed to perform the common biochemical analysis.
  22.   The system according to any one of claims 1 to 21, wherein the biochemical analysis is an analysis of molecules in a sample.
  23.   24. The system of claim 22, wherein the molecule is a polymer.
  24.   24. The system of claim 23, wherein the polymer is a polynucleotide.
  25.   23. The system of claim 22, wherein the biochemical analysis is sequencing of the polynucleotide and the output data is output sequence data representing a sequence of the polynucleotide.
  26.   26. A system according to any one of claims 1 to 25, wherein the analytical device can support a plurality of nanopores and is operable to perform a biochemical analysis of a sample using the nanopores.
  27.   The analyzer comprises electrodes configured to generate electrical signals at both ends of each nanopore, and the module is configured to generate the output data from electrical signals generated from the electrodes 27. The system of claim 26, comprising processing circuitry.
  28. The control system is
    The temperature of the analyzer,
    Electrical parameters of the biochemical analysis,
    Fluid parameters of the analyzer, or
    28. The system of claim 27, configured to change operation in a module of the cluster by changing at least one of the sampling characteristics of the output data.
  29.   27. The signal processing circuit is configured to detect events occurring at the nanopore from electrical signals at both ends of each nanopore and generate output data representing those events. 28. The system according to any one of up to 28.
  30. A module for performing biochemical analysis that can be connected to other modules via a data network,
    The module comprises an analyzer operable to perform biochemical analysis of a sample, the module configured to generate output data in at least one channel indicative of the result of the biochemical analysis; The operation of the module is controllable to change the performance of the module;
    The module includes a control unit operable to control the operation of the module, and the control unit is configured as one of an arbitrary number of modules as a cluster in order to perform a common biochemical analysis. The controller is addressable via the data network to provide an input to select and to provide an input representative of a global performance goal for the common biochemical analysis, and the controller is Is configured to control the operation of the module in response to the input,
    The control unit obtains a performance measurement value of the module from output data generated by the module at least once during the execution of the common biochemical analysis, and transmits the performance measurement value via the data network. Configured,
    (A) based on the performance measurements determined for all modules and the global performance target, changing the control in the operation of the modules of the cluster and / or (b) determined for all modules Based on performance measurements, the control unit communicates with control units in other modules of the cluster via the data network to take remedial action according to the global performance target that is not achievable. Configured as a module.
  31. A method of operating an analytical instrument to perform biochemical analysis, wherein the analytical instrument comprises a plurality of modules, each module comprising an analytical device operable to perform a biochemical analysis of a sample, A module is configured to generate output data in at least one channel representing the result of the biochemical analysis, and the operation of the module is controllable to change the performance of the module;
    Selecting any number of modules as a cluster to perform a common biochemical analysis;
    Entering global performance targets for the common biochemical analysis;
    Controlling the operation in the modules of the cluster to perform the common biochemical analysis;
    At least once during the execution of the common biochemical analysis, a performance measurement value of each module is obtained from the output data generated by the module, and (a) the performance measurement value obtained for all modules and the global performance And / or (b) changing the control in the operation of the modules of the cluster based on a goal, and / or (b) the global performance not achievable based on performance measurements determined for all the modules. Taking a remedy according to a goal.
  32. A module for performing biochemical analysis,
    An analyzer that can support a plurality of nanopores, is operable to perform a biochemical analysis of a sample using the nanopores, and comprises an electrode configured to generate an electrical signal at both ends of each nanopore When,
    A signal processing circuit configured to generate output data in a plurality of parallel channels representing a result of the biochemical analysis from an electrical signal generated from the electrode; and
    The module further comprising a control unit that is controllable to change the performance of the module and that is operable to control the operation of the module based on a performance target.
  33.   The control unit obtains a performance measurement value of the biochemical analysis at least once during execution of the biochemical analysis, and changes the control in the operation of the module based on the performance measurement value to satisfy the performance target. 33. The module of claim 32, configured as follows.
  34. The performance measurements and performance targets are
    Upper time limit for generating output data,
    The amount of output data generated, or
    34. The module of claim 33, which represents at least one of the quality of the output data that is generated.
  35. The control unit is
    The temperature of the analyzer,
    Electrical parameters of the biochemical analysis,
    Fluid parameters of the analyzer, or
    35. A module according to any one of claims 32 to 34, configured to control operation in the module of the cluster by controlling at least one of the sampling characteristics of the output data.
  36.   36. A system according to claims 32 to 35, wherein the biochemical analysis is an analysis of molecules in a sample.
  37.   40. The system of claim 36, wherein the molecule is a polymer.
  38.   38. The system of claim 37, wherein the polymer is a polynucleotide.
  39.   39. The module of claim 38, wherein the biochemical analysis is sequencing of a polynucleotide in the sample and the output data includes output sequence data representing the sequence of the polynucleotide.
  40.   The signal processing circuit is configured to detect events occurring at the nanopore from electrical signals at both ends of each nanopore and generate output data representing those events. 40. The module according to any one of 39 to 39.
  41.   41. The module according to any one of claims 32 to 40, wherein the analytical device forms an amphiphilic membrane and can support a plurality of nanopores in the analytical device.
  42.   The controller is configured to repeat during the common biochemical analysis, to determine a performance measurement of the biochemical analysis, and to change the operation of the module based on the performance measurement. 42. The system according to any one of 32 to 41.
  43.   43. Any one of claims 1-42, wherein the analyzer is controllable to change the performance of the analyzer and the controller is operable to control the operation of the analyzer. The system described in the section.
  44.   44. The system of claim 43, wherein the signal processing circuit is controllable to change the performance of the analyzer and the controller is operable to control the operation of the signal processing circuit.
  45. The signal processing circuit comprises a plurality of detection channels each capable of amplifying an electrical signal from one end of the nanopore, and the analyzer supports more nanopores than the number of detection channels; Can
    The module comprises a switch arrangement that can selectively connect the detection channels to receive electrical signals at both ends of each nanopore;
    The control unit controls a switch configuration unit for selectively connecting the detection channel to each sensor element having acceptable performance based on an amplified electrical signal output from the detection channel. 45. The system of any one of claims 32 to 44, except where configured to change operation in a module of the cluster.
  46. A method of operating a module for performing biochemical analysis, wherein the module can support a plurality of nanopores and is operable to perform biochemical analysis of a sample using the nanopores The analyzer is configured to generate an electrical signal at both ends of each nanopore, and the electrical signal generated from the electrode represents a result of the biochemical analysis in a plurality of channels. A signal processing circuit configured to generate output data,
    Controlling the operation of the module to perform biochemical analysis;
    Determining the biochemical analysis performance measurement at least once during the biochemical analysis, and altering the operation of the module based on the performance measurement to meet the performance goal.
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